diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index ac041f2..0a20c1c 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -13,7 +13,7 @@ jobs:
 
     steps:
       - uses: actions/checkout@v4
-      - uses: prefix-dev/setup-pixi@v0.8.1
+      - uses: prefix-dev/setup-pixi@v0.8.2
         with:
           environments: default
           cache: true
diff --git a/LICENSE b/LICENSE
index 08bd67f..cc36b70 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2024 SIKAI ZHANG
+Copyright (c) 2024-2025 The fastcan developers.
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal
diff --git a/doc/conf.py b/doc/conf.py
index 9e0d7ff..bc47a1b 100644
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -14,7 +14,7 @@
 # sys.path.insert(0, os.path.abspath(".."))
 # General information about the project.
 project = "fastcan"
-copyright = f"{datetime.now().year}, fastcan developers (MIT License)"
+copyright = f"2024 - {datetime.now().year}, fastcan developers (MIT License)"
 
 # The version info for the project you're documenting, acts as replacement for
 # |version| and |release|, also used in various other places throughout the
@@ -25,7 +25,7 @@
 release = importlib.metadata.version(project)
 
 # The short X.Y version.
-version = '.'.join(release.split('.')[:2])
+version = ".".join(release.split(".")[:2])
 
 # -- General configuration ---------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
diff --git a/doc/narx.rst b/doc/narx.rst
index 773572d..c08f43a 100644
--- a/doc/narx.rst
+++ b/doc/narx.rst
@@ -77,7 +77,7 @@ It should also be noted the different types of predictions in model training.
    and the model can be trained by the simple ordinary least-squares (OLS) method
 #. If assume the NARX model is a multiple-step-ahead prediction structure, the input data, like :math:`\hat{y}(k-1)` is
    unknown in advance. Therefore, the training data must first be generated by the multiple-step-ahead prediction with
-   the initial model coefficients, and then the coefficients can be updated recursively.
+   the initial model coefficients, and then the coefficients can be updated recursively
 
 ARX and OE model
 ----------------
diff --git a/doc/pruning.rst b/doc/pruning.rst
new file mode 100644
index 0000000..aa8a871
--- /dev/null
+++ b/doc/pruning.rst
@@ -0,0 +1,48 @@
+.. currentmodule:: fastcan
+
+.. _pruning:
+
+===================================================
+Dictionary learning based unsupervised data pruning
+===================================================
+
+Different from feature selection, which reduces the size of dataset in column-wise,
+data pruning reduces the size of dataset in row-wise.
+To use :class:`FastCan` for unsupervised data pruning, the target :math:`Y` matrix is
+obtained first with `dictionary learning <https://scikit-learn.org/stable/modules/decomposition.html#dictionary-learning>`_.
+Dictionary learning will learn a ``dictionary`` which is composed of atoms.
+The atoms should be very representative, so that each sample of dataset can be represented (with errors)
+by sparse linear combinations of the atoms.
+We use these atoms as the target :math:`Y` and select samples based on their correlation with :math:`Y`.
+
+One challenge to use :class:`FastCan` for data pruning is that the number to select is much larger than feature selection.
+Normally, this number is higher than the number of features, which will make the pruned data matrix singular.
+In other words, :class:`FastCan` will easily think the pruned data is redundant and no additional sample
+should be selected, as any additional samples can be represented by linear combinations of the selected samples.
+Therefore, the number to select has to be set to small.
+
+To solve this problem, we use :func:`minibatch` to loose the redundancy check of :class:`FastCan`.
+The original :class:`FastCan` checks the redunancy within :math:`X_s \in \mathbb{R}^{n\times t}`, 
+which contains :math:`t` selected samples and n features,
+and the redunancy within :math:`Y \in \mathbb{R}^{n\times m}`, which contains :math:`m` atoms :math:`y_i`.
+:func:`minibatch` ranks samples with multiple correlation coefficients between :math:`X_b \in \mathbb{R}^{n\times b}` and :math:`y_i`,
+where :math:`b` is batch size and :math:`b <= t`, instead of canonical correlation coefficients between :math:`X_s` and :math:`Y`,
+which is used in :class:`FastCan`.
+Therefore, :func:`minibatch` looses the redundancy check in two ways.
+
+#. it uses :math:`y_i` instead of :math:`Y`, so no redundancy check is performed within :math:`Y`
+#. it uses :math:`X_b` instead of :math:`X_s`, so :func:`minibatch` only checks the redundancy within a batch :math:`X_b`, but does not
+   check the redundancy between batches.
+
+
+.. rubric:: References
+
+* `"Dictionary-learning-based data pruning for system identification"
+  <https://doi.org/10.48550/arXiv.2502.11484>`_
+  Wang, T., Zhang, S., & Sun L.
+  arXiv (2025).
+
+
+.. rubric:: Examples
+
+* See :ref:`sphx_glr_auto_examples_plot_pruning.py` for an example of dictionary learning based data pruning.
diff --git a/doc/user_guide.rst b/doc/user_guide.rst
index fd4ecba..d7121de 100644
--- a/doc/user_guide.rst
+++ b/doc/user_guide.rst
@@ -13,3 +13,4 @@ User Guide
    redundancy.rst
    ols_and_omp.rst
    narx.rst
+   pruning.rst
diff --git a/examples/plot_affinity.py b/examples/plot_affinity.py
index d39d46b..ce0f912 100644
--- a/examples/plot_affinity.py
+++ b/examples/plot_affinity.py
@@ -9,7 +9,7 @@
 selection methods on affine transformed features.
 """
 
-# Authors: Sikai Zhang
+# Authors: The fastcan developers
 # SPDX-License-Identifier: MIT
 
 # %%
@@ -41,7 +41,6 @@
 print("FastCan: ", np.sort(ids_fastcan))
 
 
-
 # %%
 # Affine transformation
 # ---------------------
@@ -50,7 +49,6 @@
 # three features from the polluted features. The more stable the result, the better.
 
 
-
 n_features = X.shape[1]
 rng = np.random.default_rng()
 
@@ -75,7 +73,7 @@
 
 import matplotlib.pyplot as plt
 
-bin_lims = np.arange(n_features+1)
+bin_lims = np.arange(n_features + 1)
 counts_omp, _ = np.histogram(ids_omp_all, bins=bin_lims)
 counts_ols, _ = np.histogram(ids_ols_all, bins=bin_lims)
 counts_fastcan, _ = np.histogram(ids_fastcan_all, bins=bin_lims)
diff --git a/examples/plot_fisher.py b/examples/plot_fisher.py
index 477e35c..3df9946 100644
--- a/examples/plot_fisher.py
+++ b/examples/plot_fisher.py
@@ -10,7 +10,7 @@
 relationship with Fisher's criterion in LDA (Linear Discriminant Analysis).
 """
 
-# Authors: Sikai Zhang
+# Authors: The fastcan developers
 # SPDX-License-Identifier: MIT
 
 # %%
@@ -49,11 +49,11 @@
 fishers_criterion, _ = linalg.eigh(Sb, Sw)
 
 fishers_criterion = np.sort(fishers_criterion)[::-1]
-n_nonzero = min(X.shape[1], clf.classes_.shape[0]-1)
+n_nonzero = min(X.shape[1], clf.classes_.shape[0] - 1)
 # remove the eigenvalues which are close to zero
 fishers_criterion = fishers_criterion[:n_nonzero]
 # get canonical correlation coefficients from convert Fisher's criteria
-r2 = fishers_criterion/(1+fishers_criterion)
+r2 = fishers_criterion / (1 + fishers_criterion)
 
 # %%
 # Compute SSC
diff --git a/examples/plot_intuitive.py b/examples/plot_intuitive.py
index e1bf92f..cd524de 100644
--- a/examples/plot_intuitive.py
+++ b/examples/plot_intuitive.py
@@ -9,7 +9,7 @@
 in :class:`FastCan`.
 """
 
-# Authors: Sikai Zhang
+# Authors: The fastcan developers
 # SPDX-License-Identifier: MIT
 
 # %%
@@ -28,7 +28,6 @@
 # property, so that the usefullness of each feature can be added together without
 # redundancy.
 
-
 import matplotlib.pyplot as plt
 import numpy as np
 from matplotlib.patches import Patch
@@ -37,8 +36,9 @@
 
 from fastcan import FastCan
 
-plt.rcParams['axes.spines.right'] = False
-plt.rcParams['axes.spines.top'] = False
+plt.rcParams["axes.spines.right"] = False
+plt.rcParams["axes.spines.top"] = False
+
 
 def get_r2(feats, target, feats_selected=None):
     """Get R-squared between [feats_selected, feat_i] and target."""
@@ -54,29 +54,30 @@ def get_r2(feats, target, feats_selected=None):
         r2[i] = lr.fit(feats_i, target).score(feats_i, target)
     return r2
 
+
 def plot_bars(ids, r2_left, r2_selected):
     """Plot the relative R-squared with a bar plot."""
-    legend_selected = Patch(color='tab:green', label='X_selected')
-    legend_cand = Patch(color='tab:blue', label='x_i: candidates')
-    legend_best = Patch(color='tab:orange', label='Best candidate')
+    legend_selected = Patch(color="tab:green", label="X_selected")
+    legend_cand = Patch(color="tab:blue", label="x_i: candidates")
+    legend_best = Patch(color="tab:orange", label="Best candidate")
     n_features = len(ids)
     n_selected = len(r2_selected)
 
-    left = np.zeros(n_features)+sum(r2_selected)
+    left = np.zeros(n_features) + sum(r2_selected)
     left_selected = np.cumsum(r2_selected)
     left_selected = np.r_[0, left_selected]
     left_selected = left_selected[:-1]
     left[:n_selected] = left_selected
 
-    label = [""]*n_features
-    label[np.argmax(r2_left)+n_selected] = f"{max(r2_left):.5f}"
+    label = [""] * n_features
+    label[np.argmax(r2_left) + n_selected] = f"{max(r2_left):.5f}"
 
-    colors = ["tab:blue"]*(n_features - n_selected)
+    colors = ["tab:blue"] * (n_features - n_selected)
     colors[np.argmax(r2_left)] = "tab:orange"
-    colors = ["tab:green"]*n_selected + colors
+    colors = ["tab:green"] * n_selected + colors
 
     hbars = plt.barh(ids, width=np.r_[score_selected, r2_left], color=colors, left=left)
-    plt.axvline(x = sum(r2_selected), color = 'tab:orange', linestyle="--")
+    plt.axvline(x=sum(r2_selected), color="tab:orange", linestyle="--")
     plt.bar_label(hbars, label)
     plt.yticks(np.arange(n_features))
     plt.xlabel("R-squared between [X_selected, x_i] and y")
@@ -84,6 +85,7 @@ def plot_bars(ids, r2_left, r2_selected):
     plt.legend(handles=[legend_selected, legend_cand, legend_best])
     plt.show()
 
+
 X, y = load_diabetes(return_X_y=True)
 
 
@@ -92,7 +94,6 @@ def plot_bars(ids, r2_left, r2_selected):
 score_selected = []
 
 
-
 score_0 = get_r2(X, y)
 
 plot_bars(id_left, score_0, score_selected)
@@ -114,13 +115,12 @@ def plot_bars(ids, r2_left, r2_selected):
 id_selected += [id_left[index]]
 score_selected += [score_0[index]]
 id_left = np.delete(id_left, index)
-score_1 = get_r2(X[:, id_left], y, X[:, id_selected])-sum(score_selected)
+score_1 = get_r2(X[:, id_left], y, X[:, id_selected]) - sum(score_selected)
 
 
 plot_bars(np.r_[id_selected, id_left], score_1, score_selected)
 
 
-
 # %%
 # Select the third feature
 # ------------------------
@@ -133,12 +133,11 @@ def plot_bars(ids, r2_left, r2_selected):
 id_selected += [id_left[index]]
 score_selected += [score_1[index]]
 id_left = np.delete(id_left, index)
-score_2 = get_r2(X[:, id_left], y, X[:, id_selected])-sum(score_selected)
+score_2 = get_r2(X[:, id_left], y, X[:, id_selected]) - sum(score_selected)
 
 plot_bars(np.r_[id_selected, id_left], score_2, score_selected)
 
 
-
 # %%
 # h-correlation and eta-cosine
 # ----------------------------
@@ -180,7 +179,7 @@ def plot_bars(ids, r2_left, r2_selected):
 score_selected = [score_0[index]]
 id_left = np.arange(X.shape[1])
 id_left = np.delete(id_left, index)
-score_1_7 = get_r2(X[:, id_left], y, X[:, id_selected])-sum(score_selected)
+score_1_7 = get_r2(X[:, id_left], y, X[:, id_selected]) - sum(score_selected)
 
 plot_bars(np.r_[id_selected, id_left], score_1_7, score_selected)
 
diff --git a/examples/plot_narx.py b/examples/plot_narx.py
index 720bbcb..31e5378 100644
--- a/examples/plot_narx.py
+++ b/examples/plot_narx.py
@@ -9,7 +9,7 @@
 NARX model for time series prediction.
 """
 
-# Authors: Sikai Zhang
+# Authors: The fastcan developers
 # SPDX-License-Identifier: MIT
 
 # %%
@@ -26,19 +26,24 @@
 # :math:`u_0` and :math:`u_1` are input signals,
 # and :math:`y` is the output signal.
 
-
 import numpy as np
 
 rng = np.random.default_rng(12345)
 n_samples = 1000
 max_delay = 3
 e = rng.normal(0, 0.1, n_samples)
-u0 = rng.uniform(0, 1, n_samples+max_delay)
-u1 = rng.normal(0, 0.1, n_samples+max_delay)
-y = np.zeros(n_samples+max_delay)
-for i in range(max_delay, n_samples+max_delay):
-    y[i] = 0.5*y[i-1]+0.3*u0[i]**2+2*u0[i-1]*u0[i-3]+1.5*u0[i-2]*u1[i-3]+1
-y = y[max_delay:]+e
+u0 = rng.uniform(0, 1, n_samples + max_delay)
+u1 = rng.normal(0, 0.1, n_samples + max_delay)
+y = np.zeros(n_samples + max_delay)
+for i in range(max_delay, n_samples + max_delay):
+    y[i] = (
+        0.5 * y[i - 1]
+        + 0.3 * u0[i] ** 2
+        + 2 * u0[i - 1] * u0[i - 3]
+        + 1.5 * u0[i - 2] * u1[i - 3]
+        + 1
+    )
+y = y[max_delay:] + e
 X = np.c_[u0[max_delay:], u1[max_delay:]]
 
 # %%
@@ -75,9 +80,9 @@
 from fastcan.narx import make_time_shift_features, make_time_shift_ids
 
 time_shift_ids = make_time_shift_ids(
-    n_features=3, # Number of inputs (2) and output (1) signals
-    max_delay=3, # Maximum time delays
-    include_zero_delay = [True, True, False], # Whether to include zero delay
+    n_features=3,  # Number of inputs (2) and output (1) signals
+    max_delay=3,  # Maximum time delays
+    include_zero_delay=[True, True, False],  # Whether to include zero delay
     # for each signal. The output signal should not have zero delay.
 )
 
@@ -90,8 +95,8 @@
 from fastcan.narx import make_poly_features, make_poly_ids
 
 poly_ids = make_poly_ids(
-    n_features=time_shift_vars.shape[1], # Number of time-shifted variables
-    degree=2, # Maximum polynomial degree
+    n_features=time_shift_vars.shape[1],  # Number of time-shifted variables
+    degree=2,  # Maximum polynomial degree
 )
 
 poly_terms = make_poly_features(time_shift_vars, poly_ids)
@@ -105,7 +110,7 @@
 from fastcan import FastCan
 
 selector = FastCan(
-    n_features_to_select=4, # 4 terms should be selected
+    n_features_to_select=4,  # 4 terms should be selected
 ).fit(poly_terms, y)
 
 support = selector.get_support()
@@ -124,7 +129,7 @@
 
 narx_model = NARX(
     time_shift_ids=time_shift_ids,
-    poly_ids = selected_poly_ids,
+    poly_ids=selected_poly_ids,
 )
 
 narx_model.fit(X, y)
@@ -158,7 +163,7 @@
 
 y_pred = narx_model.predict(
     X[:100],
-    y_init=y[:narx_model.max_delay_] # Set the initial values of the prediction to
+    y_init=y[: narx_model.max_delay_],  # Set the initial values of the prediction to
     # the true values
 )
 
diff --git a/examples/plot_narx_msa.py b/examples/plot_narx_msa.py
index a7cc04a..a85861b 100644
--- a/examples/plot_narx_msa.py
+++ b/examples/plot_narx_msa.py
@@ -8,7 +8,7 @@
 In this example, we will compare one-step-ahead NARX and multi-step-ahead NARX.
 """
 
-# Authors: Sikai Zhang
+# Authors: The fastcan developers
 # SPDX-License-Identifier: MIT
 
 # %%
diff --git a/examples/plot_pruning.py b/examples/plot_pruning.py
new file mode 100644
index 0000000..cd5a0cc
--- /dev/null
+++ b/examples/plot_pruning.py
@@ -0,0 +1,114 @@
+"""
+============
+Data pruning
+============
+
+.. currentmodule:: fastcan
+
+This example shows how to prune dataset with :func:`minibatch`
+based on :class:`FastCan`.
+The method is compared to random data pruning.
+"""
+
+# Authors: The fastcan developers
+# SPDX-License-Identifier: MIT
+
+# %%
+# Load data and prepare baseline
+# ------------------------------
+# We use ``iris`` dataset and logistic regression model to demonstrate data pruning.
+# The baseline model is a logistic regression model trained on the entire dataset.
+# The coefficients of the model trained on the pruned dataset will be compared to
+# the baseline model with R-squared score.
+# The higher R-squared score, the better the pruning.
+
+from sklearn.datasets import load_iris
+from sklearn.linear_model import LogisticRegression
+
+data, labels = load_iris(return_X_y=True)
+baseline_lr = LogisticRegression(max_iter=110).fit(data, labels)
+
+# %%
+# Random data pruning
+# -------------------
+# There are 150 samples in the dataset. The pruned dataset for
+# random pruning method is selected randomly.
+
+import numpy as np
+
+
+def _random_pruning(X, y, n_samples_to_select: int, random_state: int):
+    rng = np.random.default_rng(random_state)
+    ids_random = rng.choice(y.size, n_samples_to_select, replace=False)
+    pruned_lr = LogisticRegression(max_iter=110).fit(X[ids_random], y[ids_random])
+    return pruned_lr.coef_, pruned_lr.intercept_
+
+
+# %%
+# FastCan data pruning
+# --------------------
+# To use :class:`FastCan` to prune the data, there are two steps:
+#
+# #. Learn the atoms by Dictionary Learning (here we use ``KMeans``)
+# #. Select the samples by :func:`minibatch` according to the multiple correlation
+#    between each atom and the batch of samples.
+
+from sklearn.cluster import KMeans
+
+from fastcan import minibatch
+
+
+def _fastcan_pruning(
+    X,
+    y,
+    n_samples_to_select: int,
+    random_state: int,
+    n_atoms: int,
+    batch_size: int,
+):
+    kmeans = KMeans(
+        n_clusters=n_atoms,
+        random_state=random_state,
+    ).fit(X)
+    atoms = kmeans.cluster_centers_
+    ids_fastcan = minibatch(
+        X.T, atoms.T, n_samples_to_select, batch_size=batch_size, verbose=0
+    )
+    pruned_lr = LogisticRegression(max_iter=110).fit(X[ids_fastcan], y[ids_fastcan])
+    return pruned_lr.coef_, pruned_lr.intercept_
+
+
+# %%
+# Compare pruning methods
+# -----------------------
+# 100 samples are seleced from 150 original data with ``Random`` pruning and
+# ``FastCan`` pruning. The results show that ``FastCan`` pruning gives a higher
+# mean value of R-squared and a lower standard deviation.
+
+import matplotlib.pyplot as plt
+from sklearn.metrics import r2_score
+
+
+def plot_box(X, y, baseline, n_samples_to_select: int, n_random: int):
+    r2_fastcan = np.zeros(n_random)
+    r2_random = np.zeros(n_random)
+    for i in range(n_random):
+        coef, intercept = _fastcan_pruning(
+            X, y, n_samples_to_select, i, n_atoms=50, batch_size=2
+        )
+        r2_fastcan[i] = r2_score(
+            np.c_[coef, intercept], np.c_[baseline.coef_, baseline.intercept_]
+        )
+
+        coef, intercept = _random_pruning(X, y, n_samples_to_select, i)
+        r2_random[i] = r2_score(
+            np.c_[coef, intercept], np.c_[baseline.coef_, baseline.intercept_]
+        )
+
+    plt.boxplot(np.c_[r2_fastcan, r2_random])
+    plt.ylabel("R2")
+    plt.xticks(ticks=[1, 2], labels=["FastCan", "Random"])
+    plt.show()
+
+
+plot_box(data, labels, baseline_lr, n_samples_to_select=100, n_random=10)
diff --git a/examples/plot_redundancy.py b/examples/plot_redundancy.py
index 9649794..b95aab8 100644
--- a/examples/plot_redundancy.py
+++ b/examples/plot_redundancy.py
@@ -23,7 +23,7 @@
     will be much easier.
 """
 
-# Authors: Sikai Zhang
+# Authors: The fastcan developers
 # SPDX-License-Identifier: MIT
 
 # %%
@@ -82,9 +82,10 @@ def make_redundant(
     X = rng.random((n_samples, n_features))
     y = np.dot(X[:, info_ids], informative_coef)
 
-    X[:, redundant_ids] = X[:, dep_info_ids]@redundant_coef
+    X[:, redundant_ids] = X[:, dep_info_ids] @ redundant_coef
     return X, y
 
+
 # %%
 # Define score function
 # ---------------------
@@ -97,20 +98,17 @@ def make_redundant(
 #   indices of the redundant features are :math:`[5]`, then the correctly selected
 #   indices can be any of :math:`[0, 1]`, :math:`[0, 5]`, and :math:`[1, 5]`.
 
-def get_n_missed(
-    dep_info_ids,
-    indep_info_ids,
-    redundant_ids,
-    selected_ids
-):
+
+def get_n_missed(dep_info_ids, indep_info_ids, redundant_ids, selected_ids):
     """Get the number of features miss selected."""
     n_redundant = len(redundant_ids)
     n_missed_indep = len(np.setdiff1d(indep_info_ids, selected_ids))
-    n_missed_dep = len(
-        np.setdiff1d(dep_info_ids+redundant_ids, selected_ids)
-    )-n_redundant
+    n_missed_dep = (
+        len(np.setdiff1d(dep_info_ids + redundant_ids, selected_ids)) - n_redundant
+    )
     n_missed_dep = max(n_missed_dep, 0)
-    return n_missed_indep+n_missed_dep
+    return n_missed_indep + n_missed_dep
+
 
 # %%
 # Prepare selectors
@@ -146,8 +144,8 @@ def get_n_missed(
 
 from fastcan import FastCan
 
-lsvr = LinearSVR(C = 1, dual="auto", max_iter=100000, random_state=0)
-rfr = RandomForestRegressor(n_estimators = 10, random_state=0)
+lsvr = LinearSVR(C=1, dual="auto", max_iter=100000, random_state=0)
+rfr = RandomForestRegressor(n_estimators=10, random_state=0)
 
 
 N_SAMPLES = 1000
@@ -155,7 +153,7 @@ def get_n_missed(
 DEP_INFO_IDS = [2, 4, 7, 9]
 INDEP_INFO_IDS = [0, 1, 6]
 REDUNDANT_IDS = [5, 8]
-N_SELECTED = len(DEP_INFO_IDS+INDEP_INFO_IDS)
+N_SELECTED = len(DEP_INFO_IDS + INDEP_INFO_IDS)
 N_REPEATED = 10
 
 selector_dict = {
@@ -204,8 +202,8 @@ def get_n_missed(
 
 import matplotlib.pyplot as plt
 
-fig, ax = plt.subplots(figsize = (8, 5))
-rects = ax.bar(selector_dict.keys(), n_missed.sum(0), width = 0.5)
+fig, ax = plt.subplots(figsize=(8, 5))
+rects = ax.bar(selector_dict.keys(), n_missed.sum(0), width=0.5)
 ax.bar_label(rects, n_missed.sum(0), padding=3)
 plt.xlabel("Selector")
 plt.ylabel("No. of missed features")
diff --git a/examples/plot_speed.py b/examples/plot_speed.py
index ce933dd..347f3c1 100644
--- a/examples/plot_speed.py
+++ b/examples/plot_speed.py
@@ -12,7 +12,7 @@
 
 """
 
-# Authors: Sikai Zhang
+# Authors: The fastcan developers
 # SPDX-License-Identifier: MIT
 
 # %%
@@ -69,7 +69,7 @@ def baseline(X, y, t):
     n_samples, n_features = X.shape
     mask = np.zeros(n_features, dtype=bool)
     r2 = np.zeros(n_features, dtype=float)
-    indices  = np.zeros(t, dtype=int)
+    indices = np.zeros(t, dtype=int)
     scores = np.zeros(t, dtype=float)
     X_selected = np.zeros((n_samples, 0), dtype=float)
     for i in range(t):
@@ -85,6 +85,7 @@ def baseline(X, y, t):
         X_selected = np.column_stack((X_selected, X[:, d]))
     return indices, scores
 
+
 # %%
 # Elapsed time comparison
 # -----------------------
@@ -128,12 +129,10 @@ def baseline(X, y, t):
 t_eta = timeit(
     f"s = FastCan({n_features_to_select}, eta=True, verbose=0).fit(X, y)",
     number=10,
-    globals=globals()
+    globals=globals(),
 )
 t_base = timeit(
-    f"indices, _ = baseline(X, y, {n_features_to_select})",
-    number=10,
-    globals=globals()
+    f"indices, _ = baseline(X, y, {n_features_to_select})", number=10, globals=globals()
 )
 print(f"Elapsed time using h correlation algorithm: {t_h:.5f} seconds")
 print(f"Elapsed time using eta cosine algorithm: {t_eta:.5f} seconds")
@@ -186,12 +185,12 @@ def baseline(X, y, t):
     time_eta[i] = timeit(
         f"s = FastCan({i+1}, eta=True, verbose=0).fit(X, y)",
         number=10,
-        globals=globals()
+        globals=globals(),
     )
 
-feature_num = np.arange(n_features_max, dtype=int)+1
-plt.plot(feature_num, time_h, label = "h-correlation")
-plt.plot(feature_num, time_eta, label = r'$\eta$-cosine')
+feature_num = np.arange(n_features_max, dtype=int) + 1
+plt.plot(feature_num, time_h, label="h-correlation")
+plt.plot(feature_num, time_eta, label=r"$\eta$-cosine")
 plt.title("Elapsed Time Comparison")
 plt.xlabel("Number of Selected Features")
 plt.ylabel("Elapsed Time (s)")
diff --git a/fastcan/__init__.py b/fastcan/__init__.py
index edd05f7..cdd8252 100644
--- a/fastcan/__init__.py
+++ b/fastcan/__init__.py
@@ -3,6 +3,9 @@
 algorithm.
 """
 
+# Authors: The fastcan developers
+# SPDX-License-Identifier: MIT
+
 from . import narx, utils
 from ._fastcan import FastCan
 from ._minibatch import minibatch
diff --git a/fastcan/_cancorr_fast.pyx b/fastcan/_cancorr_fast.pyx
index b0afe30..ba3fb4d 100644
--- a/fastcan/_cancorr_fast.pyx
+++ b/fastcan/_cancorr_fast.pyx
@@ -1,6 +1,9 @@
-# Author: Matthew Sikai Zhang <matthew.szhang91@gmail.com>
-#
-# Fast feature selection with sum squared canoncial correlation coefficents
+"""
+Fast feature selection with sum squared canoncial correlation coefficents
+"""
+# Authors: The fastcan developers
+# SPDX-License-Identifier: MIT
+
 from libc.stdlib cimport malloc, free
 from libc.string cimport memset
 from cython cimport floating, final
diff --git a/fastcan/_fastcan.py b/fastcan/_fastcan.py
index 611d9d4..2441858 100644
--- a/fastcan/_fastcan.py
+++ b/fastcan/_fastcan.py
@@ -2,6 +2,9 @@
 Feature selection
 """
 
+# Authors: The fastcan developers
+# SPDX-License-Identifier: MIT
+
 from copy import deepcopy
 from numbers import Integral, Real
 
diff --git a/fastcan/_minibatch.py b/fastcan/_minibatch.py
index f01879b..3f1c36d 100644
--- a/fastcan/_minibatch.py
+++ b/fastcan/_minibatch.py
@@ -2,6 +2,9 @@
 Feature selection with mini-batch.
 """
 
+# Authors: The fastcan developers
+# SPDX-License-Identifier: MIT
+
 from copy import deepcopy
 from numbers import Integral
 
@@ -54,7 +57,7 @@ def minibatch(X, y, n_features_to_select=1, batch_size=1, verbose=1):
         The parameter is the absolute number of features to select.
 
     batch_size : int, default=1
-        The number of features in a mini-batch.
+        The upper bound of the number of features in a mini-batch.
         It is recommended that batch_size be less than n_samples.
 
     verbose : int, default=1
@@ -110,17 +113,23 @@ def minibatch(X, y, n_features_to_select=1, batch_size=1, verbose=1):
                 indices_include,
                 indices_select,
             )
-            _forward_search(
-                X=deepcopy(X_transformed_),
-                V=y_i,
-                t=batch_size_temp,
-                tol=0.01,
-                num_threads=n_threads,
-                verbose=0,
-                mask=mask,
-                indices=indices,
-                scores=scores,
-            )
+            try:
+                _forward_search(
+                    X=deepcopy(X_transformed_),
+                    V=y_i,
+                    t=batch_size_temp,
+                    tol=0.01,
+                    num_threads=n_threads,
+                    verbose=0,
+                    mask=mask,
+                    indices=indices,
+                    scores=scores,
+                )
+            except RuntimeError:
+                # If the batch size is too large, _forward_search cannot find enough
+                # samples to form a non-singular matrix. Then, reduce the batch size.
+                indices = indices[indices != -1]
+                batch_size_temp = indices.size
             indices_select = np.r_[indices_select, indices]
             n_selected_i += batch_size_temp
             if verbose == 1:
diff --git a/fastcan/_refine.py b/fastcan/_refine.py
index c0ef85d..0dbd95a 100644
--- a/fastcan/_refine.py
+++ b/fastcan/_refine.py
@@ -2,6 +2,9 @@
 Refine fastcan selection results
 """
 
+# Authors: The fastcan developers
+# SPDX-License-Identifier: MIT
+
 from copy import deepcopy
 from numbers import Integral
 
diff --git a/fastcan/narx.py b/fastcan/narx.py
index 37dd8fe..e24bbcc 100644
--- a/fastcan/narx.py
+++ b/fastcan/narx.py
@@ -3,6 +3,9 @@
 identification.
 """
 
+# Authors: The fastcan developers
+# SPDX-License-Identifier: MIT
+
 import math
 from itertools import combinations_with_replacement
 from numbers import Integral
diff --git a/fastcan/utils.py b/fastcan/utils.py
index ab9467a..fec6e73 100644
--- a/fastcan/utils.py
+++ b/fastcan/utils.py
@@ -1,5 +1,8 @@
 """Utils functions."""
 
+# Authors: The fastcan developers
+# SPDX-License-Identifier: MIT
+
 from numbers import Integral
 
 import numpy as np
diff --git a/pixi.lock b/pixi.lock
index 828d9f6..7590849 100644
--- a/pixi.lock
+++ b/pixi.lock
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-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cython-3.0.11-py313hc66aa0d_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2025.1.31-hbcca054_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cython-3.0.12-py313h5dec8f5_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_1.conda
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-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-26_linux64_openblas.conda
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       osx-64:
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       - conda: https://conda.anaconda.org/conda-forge/osx-64/cxx-compiler-1.9.0-h20888b2_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-64/cython-3.0.11-py313h496bac6_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-64/cython-3.0.12-py313h9efc8c2_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/fortran-compiler-1.9.0-h02557f8_0.conda
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@@ -76,73 +76,73 @@ environments:
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-- pypi: https://files.pythonhosted.org/packages/e6/57/4cc35004605416df3225ff362f3455cf09765db00df578ae9e46d0fefd23/fonttools-4.55.3-cp313-cp313-win_amd64.whl
+- pypi: https://files.pythonhosted.org/packages/be/6a/fd4018e0448c8a5e12138906411282c5eab51a598493f080a9f0960e658f/fonttools-4.56.0-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl
   name: fonttools
-  version: 4.55.3
-  sha256: 11e5de1ee0d95af4ae23c1a138b184b7f06e0b6abacabf1d0db41c90b03d834b
+  version: 4.56.0
+  sha256: a05d1f07eb0a7d755fbe01fee1fd255c3a4d3730130cf1bfefb682d18fd2fcea
   requires_dist:
   - fs>=2.2.0,<3 ; extra == 'ufo'
   - lxml>=4.0 ; extra == 'lxml'
@@ -2301,18 +2307,18 @@ packages:
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   sha256: 0d8d18d08f840c19d0ee7ca1fd82490fdc3729b7ac93f49870406ddde8ef8d8b
   requires_python: '>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*'
-- conda: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.5.0-pyha770c72_1.conda
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-  md5: 315607a3030ad5d5227e76e0733798ff
+- conda: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.6.1-pyha770c72_0.conda
+  sha256: 598951ebdb23e25e4cec4bbff0ae369cec65ead80b50bc08b441d8e54de5cf03
+  md5: f4b39bf00c69f56ac01e020ebfac066c
   depends:
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   - zipp >=0.5
   license: Apache-2.0
   license_family: APACHE
   purls:
-  - pkg:pypi/importlib-metadata?source=hash-mapping
-  size: 28623
-  timestamp: 1733223207185
+  - pkg:pypi/importlib-metadata?source=compressed-mapping
+  size: 29141
+  timestamp: 1737420302391
 - conda: https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_1.conda
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   md5: 6837f3eff7dcea42ecd714ce1ac2b108
@@ -2407,11 +2413,11 @@ packages:
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   sha256: a17b7c4f5b2c51bb68ed379defd608a03954a1845dfed7cc0117f1cc8a9b7fd2
   requires_python: '>=3.10'
-- conda: https://conda.anaconda.org/conda-forge/osx-64/ld64-951.9-h4e51db5_2.conda
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-  md5: 7c611059c79bc9e291cfcd58d2c30af8
+- conda: https://conda.anaconda.org/conda-forge/osx-64/ld64-951.9-h4e51db5_3.conda
+  sha256: 36e5a42c7aebad58d629c6bc1cb36243653a935e609e03a89b22005aa4ab2ddb
+  md5: d1743e343b8e13a4980dac16f050d2b6
   depends:
-  - ld64_osx-64 951.9 hc8d1a19_2
+  - ld64_osx-64 951.9 h33512f0_3
   - libllvm18 >=18.1.8,<18.2.0a0
   constrains:
   - cctools 1010.6.*
@@ -2421,13 +2427,13 @@ packages:
   license: APSL-2.0
   license_family: Other
   purls: []
-  size: 18425
-  timestamp: 1732552402890
-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/ld64-951.9-h4c6efb1_2.conda
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-  md5: aa1b9f30c29d9f23a916fbde02369525
+  size: 18577
+  timestamp: 1738620853804
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/ld64-951.9-h4c6efb1_3.conda
+  sha256: 85e239ab3c1fdf491e32d797995e5f4b7873e055aebcd2d1fdcb29cede1a6a10
+  md5: 0fa58d2a5918cc82fc409e4f6973a02c
   depends:
-  - ld64_osx-arm64 951.9 hfc0fa09_2
+  - ld64_osx-arm64 951.9 hb6b49e2_3
   - libllvm18 >=18.1.8,<18.2.0a0
   constrains:
   - cctools 1010.6.*
@@ -2437,11 +2443,11 @@ packages:
   license: APSL-2.0
   license_family: Other
   purls: []
-  size: 18471
-  timestamp: 1732552789618
-- conda: https://conda.anaconda.org/conda-forge/osx-64/ld64_osx-64-951.9-hc8d1a19_2.conda
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-  md5: 5a5b6e8ef84119997f8e1c99cc73d233
+  size: 18607
+  timestamp: 1738620734041
+- conda: https://conda.anaconda.org/conda-forge/osx-64/ld64_osx-64-951.9-h33512f0_3.conda
+  sha256: 4e6a37e3ea23a3d2f9b25b1b476728be6b5a1bab326fa5a976997698c60f70c0
+  md5: f5c97cad6996928bdffc55b8f5e70723
   depends:
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   - libcxx
@@ -2451,18 +2457,18 @@ packages:
   constrains:
   - clang >=18.1.8,<19.0a0
   - ld 951.9.*
-  - cctools 1010.6.*
   - cctools_osx-64 1010.6.*
+  - cctools 1010.6.*
   arch: x86_64
   platform: osx
   license: APSL-2.0
   license_family: Other
   purls: []
-  size: 1096442
-  timestamp: 1732552330373
-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/ld64_osx-arm64-951.9-hfc0fa09_2.conda
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-  md5: 8d771aab63c19d4df4b29cac99dde70c
+  size: 1100751
+  timestamp: 1738620763081
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/ld64_osx-arm64-951.9-hb6b49e2_3.conda
+  sha256: d79dbb1016fac31ee90b3b5202270c1d331bd9987d6418b35fda473fcde6c126
+  md5: 34d3acfffa837eef8c2912cd7c2156f2
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   - libcxx
@@ -2470,17 +2476,17 @@ packages:
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   - tapi >=1300.6.5,<1301.0a0
   constrains:
-  - cctools 1010.6.*
   - cctools_osx-arm64 1010.6.*
-  - clang >=18.1.8,<19.0a0
+  - cctools 1010.6.*
   - ld 951.9.*
+  - clang >=18.1.8,<19.0a0
   arch: arm64
   platform: osx
   license: APSL-2.0
   license_family: Other
   purls: []
-  size: 1020475
-  timestamp: 1732552694350
+  size: 1018029
+  timestamp: 1738620654864
 - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.43-h712a8e2_2.conda
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@@ -2495,152 +2501,155 @@ packages:
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-  md5: ac52800af2e0c0e7dac770b435ce768a
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-28_h59b9bed_openblas.conda
+  build_number: 28
+  sha256: 93fbcf2800b859b7ca5add3ab5d3aa11c6a6ff4b942a1cea4bf644f78488edb8
+  md5: 73e2a99fdeb8531d50168987378fda8a
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   - libopenblas >=0.3.28,<1.0a0
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-  - liblapack 3.9.0 26_linux64_openblas
-  - liblapacke 3.9.0 26_linux64_openblas
-  - blas * openblas
+  - libcblas =3.9.0=28*_openblas
+  - blas =2.128=openblas
+  - liblapack =3.9.0=28*_openblas
+  - liblapacke =3.9.0=28*_openblas
+  - mkl <2025
   arch: x86_64
   platform: linux
   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 16393
-  timestamp: 1734432564346
-- conda: https://conda.anaconda.org/conda-forge/osx-64/libblas-3.9.0-26_osx64_openblas.conda
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-  md5: 2f03da7a6d52d98bbea1f7390d6997bf
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+- conda: https://conda.anaconda.org/conda-forge/osx-64/libblas-3.9.0-30_h7f60823_openblas.conda
+  build_number: 30
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+  - libopenblas >=0.3.29,<0.3.30.0a0
+  - libopenblas >=0.3.29,<1.0a0
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-  - liblapack 3.9.0 26_osx64_openblas
-  - blas * openblas
-  - liblapacke 3.9.0 26_osx64_openblas
+  - liblapack =3.9.0=30*_openblas
+  - libcblas =3.9.0=30*_openblas
+  - liblapacke =3.9.0=30*_openblas
+  - blas =2.130=openblas
   arch: x86_64
   platform: osx
   license: BSD-3-Clause
   license_family: BSD
   purls: []
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-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libblas-3.9.0-26_osxarm64_openblas.conda
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-  md5: 21be102c9ae80a67ba7de23b129aa7f6
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+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libblas-3.9.0-30_h10e41b3_openblas.conda
+  build_number: 30
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+  md5: 1317dcfcde16781b5d446bfb4c81b848
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-  - libopenblas >=0.3.28,<1.0a0
+  - libopenblas >=0.3.29,<0.3.30.0a0
+  - libopenblas >=0.3.29,<1.0a0
   constrains:
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-  - liblapacke 3.9.0 26_osxarm64_openblas
-  - libcblas 3.9.0 26_osxarm64_openblas
-  - blas * openblas
+  - libcblas =3.9.0=30*_openblas
+  - liblapack =3.9.0=30*_openblas
+  - blas =2.130=openblas
+  - liblapacke =3.9.0=30*_openblas
   arch: arm64
   platform: osx
   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 16714
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-- conda: https://conda.anaconda.org/conda-forge/win-64/libblas-3.9.0-26_win64_mkl.conda
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-  md5: ecfe732dbad1be001826fdb7e5e891b5
+  size: 17013
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+- conda: https://conda.anaconda.org/conda-forge/win-64/libblas-3.9.0-30_h576b46c_mkl.conda
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+  md5: 3f3333b80dbf50fcffa6626cd970a7d3
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   constrains:
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-  - liblapack 3.9.0 26_win64_mkl
-  - blas * mkl
-  - libcblas 3.9.0 26_win64_mkl
+  - libcblas =3.9.0=30*_mkl
+  - liblapack =3.9.0=30*_mkl
+  - liblapacke =3.9.0=30*_mkl
+  - blas =2.130=mkl
   arch: x86_64
   platform: win
   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 3733122
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-  md5: ebcc5f37a435aa3c19640533c82f8d76
+  size: 3732759
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+- conda: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-28_he106b2a_openblas.conda
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+  - libblas 3.9.0 28_h59b9bed_openblas
   constrains:
-  - liblapack 3.9.0 26_linux64_openblas
-  - liblapacke 3.9.0 26_linux64_openblas
-  - blas * openblas
+  - blas =2.128=openblas
+  - liblapack =3.9.0=28*_openblas
+  - liblapacke =3.9.0=28*_openblas
+  - mkl <2025
   arch: x86_64
   platform: linux
   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 16336
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-  md5: 8726a2949c303b23da89be658a19675c
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+- conda: https://conda.anaconda.org/conda-forge/osx-64/libcblas-3.9.0-30_hff6cab4_openblas.conda
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-  - liblapack 3.9.0 26_osx64_openblas
-  - blas * openblas
-  - liblapacke 3.9.0 26_osx64_openblas
+  - liblapack =3.9.0=30*_openblas
+  - liblapacke =3.9.0=30*_openblas
+  - blas =2.130=openblas
   arch: x86_64
   platform: osx
   license: BSD-3-Clause
   license_family: BSD
   purls: []
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-  md5: a0e9980fe12d42f6d0c0ec009f67e948
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+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcblas-3.9.0-30_hb3479ef_openblas.conda
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   depends:
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+  - libblas 3.9.0 30_h10e41b3_openblas
   constrains:
-  - liblapack 3.9.0 26_osxarm64_openblas
-  - liblapacke 3.9.0 26_osxarm64_openblas
-  - blas * openblas
+  - liblapack =3.9.0=30*_openblas
+  - blas =2.130=openblas
+  - liblapacke =3.9.0=30*_openblas
   arch: arm64
   platform: osx
   license: BSD-3-Clause
   license_family: BSD
   purls: []
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-- conda: https://conda.anaconda.org/conda-forge/win-64/libcblas-3.9.0-26_win64_mkl.conda
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-  md5: 652f3adcb9d329050a325416edb14246
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+- conda: https://conda.anaconda.org/conda-forge/win-64/libcblas-3.9.0-30_h7ad3364_mkl.conda
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+  md5: d00c43cf61eddb8f694dfc13c7885244
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+  - libblas 3.9.0 30_h576b46c_mkl
+  - mkl >=2024.2.2,<2025.0a0
   constrains:
-  - liblapacke 3.9.0 26_win64_mkl
-  - liblapack 3.9.0 26_win64_mkl
-  - blas * mkl
+  - liblapack =3.9.0=30*_mkl
+  - liblapacke =3.9.0=30*_mkl
+  - blas =2.130=mkl
   arch: x86_64
   platform: win
   license: BSD-3-Clause
   license_family: BSD
   purls: []
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-- conda: https://conda.anaconda.org/conda-forge/osx-64/libclang-cpp18.1-18.1.8-default_h0c94c6a_5.conda
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-  md5: a03d49b4f1b1a9d8904f5ee7cc715967
+  size: 3732784
+  timestamp: 1739836771723
+- conda: https://conda.anaconda.org/conda-forge/osx-64/libclang-cpp18.1-18.1.8-default_h3571c67_7.conda
+  sha256: 81917105ee2f49e85f0abd684696ad9d9a81aef9d825e563e0013fd9a1ccbd23
+  md5: d22bdc2b1ecf45631c5aad91f660623a
   depends:
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   license_family: Apache
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-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libclang-cpp18.1-18.1.8-default_h5c12605_5.conda
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-  md5: 871576a2d3e0c8c3d0278e1085e74914
+  size: 13901227
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+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libclang-cpp18.1-18.1.8-default_hf90f093_7.conda
+  sha256: f3abadc7fa0a98814c07644b8117ac25d03a1cb78be8ea40a32bcd2dede5b3c9
+  md5: 3a2e0aa2daf3b1a009b87fd7fa5d4a41
   depends:
   - __osx >=11.0
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@@ -2664,8 +2673,8 @@ packages:
   license: Apache-2.0 WITH LLVM-exception
   license_family: Apache
   purls: []
-  size: 12775616
-  timestamp: 1726862192152
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+  md5: e3eb7806380bc8bcecba6d749ad5f026
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+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=13
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   license: MIT
   license_family: MIT
   purls: []
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-- conda: https://conda.anaconda.org/conda-forge/osx-64/libffi-3.4.2-h0d85af4_5.tar.bz2
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-  md5: ccb34fb14960ad8b125962d3d79b31a9
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+  md5: b8667b0d0400b8dcb6844d8e06b2027d
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   arch: x86_64
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-  size: 51348
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-  - vs2015_runtime >=14.16.27012
+  - ucrt >=10.0.20348.0
+  - vc >=14.2,<15
+  - vc14_runtime >=14.29.30139
   arch: x86_64
   platform: win
   license: MIT
   license_family: MIT
   purls: []
-  size: 42063
-  timestamp: 1636489106777
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+- conda: https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-28_h7ac8fdf_openblas.conda
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-  - libcblas 3.9.0 26_linux64_openblas
-  - liblapacke 3.9.0 26_linux64_openblas
-  - blas * openblas
+  - libcblas =3.9.0=28*_openblas
+  - blas =2.128=openblas
+  - liblapacke =3.9.0=28*_openblas
+  - mkl <2025
   arch: x86_64
   platform: linux
   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 16338
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-  md5: c0c54bb6382ff1e52bf08f1da539e9b4
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+- conda: https://conda.anaconda.org/conda-forge/osx-64/liblapack-3.9.0-30_h236ab99_openblas.conda
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-  - libcblas 3.9.0 26_osx64_openblas
-  - blas * openblas
-  - liblapacke 3.9.0 26_osx64_openblas
+  - blas =2.130=openblas
+  - libcblas =3.9.0=30*_openblas
+  - liblapacke =3.9.0=30*_openblas
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   platform: osx
   license: BSD-3-Clause
   license_family: BSD
   purls: []
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-  - libcblas 3.9.0 26_osxarm64_openblas
-  - blas * openblas
+  - libcblas =3.9.0=30*_openblas
+  - blas =2.130=openblas
+  - liblapacke =3.9.0=30*_openblas
   arch: arm64
   platform: osx
   license: BSD-3-Clause
   license_family: BSD
   purls: []
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+- conda: https://conda.anaconda.org/conda-forge/win-64/liblapack-3.9.0-30_hacfb0e4_mkl.conda
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+  - libblas 3.9.0 30_h576b46c_mkl
+  - mkl >=2024.2.2,<2025.0a0
   constrains:
-  - liblapacke 3.9.0 26_win64_mkl
-  - blas * mkl
-  - libcblas 3.9.0 26_win64_mkl
+  - libcblas =3.9.0=30*_mkl
+  - blas =2.130=mkl
+  - liblapacke =3.9.0=30*_mkl
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   platform: win
   license: BSD-3-Clause
   license_family: BSD
   purls: []
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-  md5: 26d0c419fa96d703f9728c39e2727196
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+  timestamp: 1739836816172
+- conda: https://conda.anaconda.org/conda-forge/osx-64/libllvm18-18.1.8-hc29ff6c_3.conda
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+  md5: a04c2fc058fd6b0630c1a2faad322676
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-  - libcxx >=16
-  - libxml2 >=2.12.7,<3.0a0
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   - libzlib >=1.3.1,<2.0a0
   - zstd >=1.5.6,<1.6.0a0
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   license_family: Apache
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-  - libcxx >=16
-  - libxml2 >=2.12.7,<3.0a0
+  - libcxx >=18
+  - libxml2 >=2.13.5,<3.0a0
   - libzlib >=1.3.1,<2.0a0
   - zstd >=1.5.6,<1.6.0a0
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   license_family: Apache
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+  md5: 42d5b6a0f30d3c10cd88cb8584fda1cb
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+  - openblas >=0.3.29,<0.3.30.0a0
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+  - openblas >=0.3.29,<0.3.30.0a0
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   platform: osx
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   license: Unlicense
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+  name: typing-extensions
+  version: 4.12.2
+  sha256: 04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d
+  requires_python: '>=3.8'
 - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.12.2-pyha770c72_1.conda
   sha256: 337be7af5af8b2817f115b3b68870208b30c31d3439bec07bfb2d8f4823e3568
   md5: d17f13df8b65464ca316cbc000a3cb64
@@ -5993,13 +6012,13 @@ packages:
   license_family: PSF
   size: 39637
   timestamp: 1733188758212
-- conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2024b-hc8b5060_0.conda
-  sha256: 4fde5c3008bf5d2db82f2b50204464314cc3c91c1d953652f7bd01d9e52aefdf
-  md5: 8ac3367aafb1cc0a068483c580af8015
+- conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025a-h78e105d_0.conda
+  sha256: c4b1ae8a2931fe9b274c44af29c5475a85b37693999f8c792dad0f8c6734b1de
+  md5: dbcace4706afdfb7eb891f7b37d07c04
   license: LicenseRef-Public-Domain
   purls: []
-  size: 122354
-  timestamp: 1728047496079
+  size: 122921
+  timestamp: 1737119101255
 - conda: https://conda.anaconda.org/conda-forge/win-64/ucrt-10.0.22621.0-h57928b3_1.conda
   sha256: db8dead3dd30fb1a032737554ce91e2819b43496a0db09927edf01c32b577450
   md5: 6797b005cd0f439c4c5c9ac565783700
@@ -6022,9 +6041,9 @@ packages:
   - pysocks>=1.5.6,!=1.5.7,<2.0 ; extra == 'socks'
   - zstandard>=0.18.0 ; extra == 'zstd'
   requires_python: '>=3.9'
-- conda: https://conda.anaconda.org/conda-forge/linux-64/uv-0.5.20-h0f3a69f_0.conda
-  sha256: 0642806f1cdd18c3612baadd2ca2b377caf721167a83255f14000dc84c70ffd5
-  md5: 5210fc1e33587aeafedf4536b8370758
+- conda: https://conda.anaconda.org/conda-forge/linux-64/uv-0.6.1-h0f3a69f_0.conda
+  sha256: d907da7d13a4160156fe3f03a0023ad7b5b6977246c8ef878622ab02498a87f1
+  md5: 192e40749dfda674d691bf21ba5263f9
   depends:
   - __glibc >=2.17,<3.0.a0
   - libgcc >=13
@@ -6035,11 +6054,11 @@ packages:
   platform: linux
   license: Apache-2.0 OR MIT
   purls: []
-  size: 11147321
-  timestamp: 1736988350575
-- conda: https://conda.anaconda.org/conda-forge/osx-64/uv-0.5.20-h8de1528_0.conda
-  sha256: d93569f412bcbd786f80834e5e9587855cca8a4fc505edcf6b34a4facee702f6
-  md5: 496723ff5f2dafe1b31bdcb172205eb4
+  size: 11510888
+  timestamp: 1739827201261
+- conda: https://conda.anaconda.org/conda-forge/osx-64/uv-0.6.1-h8de1528_0.conda
+  sha256: 31883bd5b811ded91a8b657ebf04685fd469c6c563c6c56df3abb55c6c9efc98
+  md5: 48bdb8c9ea00c5a9bea500725a0be14f
   depends:
   - __osx >=10.13
   - libcxx >=18
@@ -6049,11 +6068,11 @@ packages:
   platform: osx
   license: Apache-2.0 OR MIT
   purls: []
-  size: 10693624
-  timestamp: 1736989586034
-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/uv-0.5.20-h668ec48_0.conda
-  sha256: 680468b472392ecb8a64e960fd8f68ccb49f6032a2c06d6d2d0aebff8be8c6c4
-  md5: 999824896469f042c704b1fb2b35e243
+  size: 10865011
+  timestamp: 1739828255156
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/uv-0.6.1-h668ec48_0.conda
+  sha256: 83e616f935e06c5901f40cc3faf04528e851e08a77eb313cb3b53e6a25edae09
+  md5: 56ab3b0ed99e078a4eafa0929021fbe5
   depends:
   - __osx >=11.0
   - libcxx >=18
@@ -6063,11 +6082,11 @@ packages:
   platform: osx
   license: Apache-2.0 OR MIT
   purls: []
-  size: 10694911
-  timestamp: 1736989379596
-- conda: https://conda.anaconda.org/conda-forge/win-64/uv-0.5.20-ha08ef0e_0.conda
-  sha256: 3f3752e1cadded61e556de29ad6937f2d04bebe9569d50b106f8b4ae18f75f22
-  md5: 4aa469537fe8fa3e671acefd69baf32e
+  size: 10050909
+  timestamp: 1739828408239
+- conda: https://conda.anaconda.org/conda-forge/win-64/uv-0.6.1-ha08ef0e_0.conda
+  sha256: b69ad9b9a36a4097125d7b96e030f767f06ea2c53e5bae1b4526182ca553342d
+  md5: 4ecf313b38b56a64d89e88b0c9fcdb01
   depends:
   - ucrt >=10.0.20348.0
   - vc >=14.2,<15
@@ -6076,13 +6095,13 @@ packages:
   platform: win
   license: Apache-2.0 OR MIT
   purls: []
-  size: 11330039
-  timestamp: 1736989143594
-- conda: https://conda.anaconda.org/conda-forge/win-64/vc-14.3-ha32ba9b_23.conda
-  sha256: 986ddaf8feec2904eac9535a7ddb7acda1a1dfb9482088fdb8129f1595181663
-  md5: 7c10ec3158d1eb4ddff7007c9101adb0
+  size: 11897318
+  timestamp: 1739828123220
+- conda: https://conda.anaconda.org/conda-forge/win-64/vc-14.3-h5fd82a7_24.conda
+  sha256: 7ce178cf139ccea5079f9c353b3d8415d1d49b0a2f774662c355d3f89163d7b4
+  md5: 00cf3a61562bd53bd5ea99e6888793d0
   depends:
-  - vc14_runtime >=14.38.33135
+  - vc14_runtime >=14.40.33810
   arch: x86_64
   platform: win
   track_features:
@@ -6090,34 +6109,22 @@ packages:
   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 17479
-  timestamp: 1731710827215
-- conda: https://conda.anaconda.org/conda-forge/win-64/vc14_runtime-14.42.34433-he29a5d6_23.conda
-  sha256: c483b090c4251a260aba6ff3e83a307bcfb5fb24ad7ced872ab5d02971bd3a49
-  md5: 32b37d0cfa80da34548501cdc913a832
+  size: 17693
+  timestamp: 1737627189024
+- conda: https://conda.anaconda.org/conda-forge/win-64/vc14_runtime-14.42.34433-h6356254_24.conda
+  sha256: abda97b8728cf6e3c37df8f1178adde7219bed38b96e392cb3be66336386d32e
+  md5: 2441e010ee255e6a38bf16705a756e94
   depends:
   - ucrt >=10.0.20348.0
   constrains:
-  - vs2015_runtime 14.42.34433.* *_23
+  - vs2015_runtime 14.42.34433.* *_24
   arch: x86_64
   platform: win
   license: LicenseRef-MicrosoftVisualCpp2015-2022Runtime
   license_family: Proprietary
   purls: []
-  size: 754247
-  timestamp: 1731710681163
-- conda: https://conda.anaconda.org/conda-forge/win-64/vs2015_runtime-14.42.34433-hdffcdeb_23.conda
-  sha256: 568ce8151eaae256f1cef752fc78651ad7a86ff05153cc7a4740b52ae6536118
-  md5: 5c176975ca2b8366abad3c97b3cd1e83
-  depends:
-  - vc14_runtime >=14.42.34433
-  arch: x86_64
-  platform: win
-  license: BSD-3-Clause
-  license_family: BSD
-  purls: []
-  size: 17572
-  timestamp: 1731710685291
+  size: 753531
+  timestamp: 1737627061911
 - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.21.0-pyhd8ed1ab_1.conda
   sha256: 567c04f124525c97a096b65769834b7acb047db24b15a56888a322bf3966c3e1
   md5: 0c3cc595284c5e8f0f9900a9b228a332
diff --git a/pyproject.toml b/pyproject.toml
index fe200ef..0fd16da 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -162,8 +162,12 @@ exclude = [
 ]
 
 [tool.ruff.lint]
+# This enables us to use CPY001: copyright header check
+preview = true
+# This enables us to use the explicit preview rules that we want only
+explicit-preview-rules = true
 # all rules can be found here: https://beta.ruff.rs/docs/rules/
-select = ["E", "F", "W", "I"]
+select = ["E", "F", "W", "I", "CPY001"]
 
 ignore = [
     # space before : (needed for how black formats slicing)
@@ -174,11 +178,16 @@ ignore = [
     "E741",
 ]
 
+[tool.ruff.lint.flake8-copyright]
+notice-rgx = "\\#\\ Authors:\\ The\\ fastcan\\ developers\\\r?\\\n\\#\\ SPDX\\-License\\-Identifier:\\ MIT"
+
 [tool.ruff.lint.per-file-ignores]
 # It's fine not to put the import at the top of the file in the examples
 # folder.
 "examples/*" = ["E402"]
 "doc/conf.py" = ["E402"]
+"tests/*" = ["CPY001"]
+"doc/*" = ["CPY001"]
 
 
 [tool.cython-lint]
diff --git a/tests/test_minibatch.py b/tests/test_minibatch.py
index 9018a09..c650ecd 100644
--- a/tests/test_minibatch.py
+++ b/tests/test_minibatch.py
@@ -1,12 +1,34 @@
 """Test feature selection with mini-batch"""
 import numpy as np
 import pytest
-from sklearn.datasets import make_classification
+from sklearn.cluster import KMeans
+from sklearn.datasets import load_iris, make_classification
 from sklearn.preprocessing import OneHotEncoder
 
 from fastcan import minibatch
 
 
+def test_data_pruning():
+    # Test large batch size issue for data pruning
+    n_atoms = 10
+    random_state = 0
+    n_to_select = 100
+    batch_size = 5
+
+    X, _ = load_iris(return_X_y=True)
+    kmeans = KMeans(
+        n_clusters=n_atoms,
+        random_state=random_state,
+    ).fit(X)
+    atoms = kmeans.cluster_centers_
+    indices = minibatch(
+        X.T, atoms.T, n_to_select, batch_size=batch_size, verbose=0
+    )
+    assert np.unique(indices).size == n_to_select
+    assert indices.size == n_to_select
+
+
+
 def test_select_minibatch_cls():
     # Test whether refine work correctly with random samples.
     n_samples = 200