Addressing docs suggestions for inclusion of PCovC

Author: rvasav26

docs/src/getting-started.rst

@@ -37,10 +37,10 @@ Notebook Examples
 .. include:: examples/reconstruction/index.rst
    :start-line: 4
 
-.. _getting_started-pcovr:
+.. _getting_started-hybrid:
 
-Principal Covariates Regression
--------------------------------
+Hybrid Mapping Techniques
+-------------------------
 
 .. automodule:: skmatter.decomposition
    :noindex:
@@ -50,3 +50,5 @@ Notebook Examples
 
 .. include:: examples/pcovr/index.rst
    :start-line: 4
+.. include:: examples/pcovc/index.rst
+   :start-line: 4

docs/src/index.rst

@@ -33,15 +33,15 @@
 
 .. only:: html
 
-  :ref:`getting_started-pcovr`
+  :ref:`getting_started-hybrid`
 
   .. image:: /examples/pcovr/images/thumb/sphx_glr_PCovR_thumb.png
     :alt:
 
 .. raw:: html
 
                     </h5>
-                    <p class="card-text">Utilises a combination between a PCA-like and a LR-like loss
+                    <p class="card-text">PCovR and PCovC utilize a combination between a PCA-like and a LR-like loss
                     to determine the decomposition matrix to project feature into latent space</p>
                 </div>
                 </div>

docs/src/references/decomposition.rst

@@ -1,5 +1,5 @@
-Hybrid Mapping Techniques (PCovR and PCovC)
-===========================================
+Hybrid Mapping Techniques
+=========================
 
 .. _PCovR-api:
 

src/skmatter/decomposition/__init__.py

@@ -3,12 +3,18 @@
 in order to compress data or visualise trends in the dataset. In the archetypal
 method for this dimensionality reduction, principal components analysis (PCA),
 features are transformed into the latent space which best preserves the
-variance of the original data. This module provides the Principal Covariates
-Regression (PCovR), as introduced by [deJong1992]_, is a modification to PCA
+variance of the original data.
+
+This module provides the Principal Covariates
+Regression (PCovR), as introduced by [deJong1992]_, which is a modification to PCA
 that incorporates target information, such that the resulting embedding could
 be tuned using a mixing parameter α to improve performance in regression tasks
 (:math:`\alpha = 0` corresponding to linear regression and :math:`\alpha = 1`
-corresponding to PCA).  [Helfrecht2020]_ introduced the non-linear version,
+corresponding to PCA). Also provided is Principal Covariates Classification (PCovC),
+proposed in [Jorgensen2025]_, which adapts PCovR for use in classification tasks by
+leveraging the evidence :math:`\mathbf{Z}` as an approximation of :math:`\mathbf{Y}`.
+
+[Helfrecht2020]_ introduced the non-linear version of PCovR,
 Kernel Principal Covariates Regression (KPCovR), where the mixing parameter α
 now interpolates between kernel ridge regression (:math:`\alpha = 0`) and
 kernel principal components analysis (KPCA, :math:`\alpha = 1`).
@@ -20,8 +26,10 @@
   a low-dimensional projection of the feature vectors that simultaneously minimises
   information loss and error in predicting the target properties using only the
   latent space vectors :math:`\mathbf{T}`.
-* :ref:`KPCovR-api` the Kernel Principal Covariates Regression
-  a kernel-based variation on the
+* :ref:`PCovC-api` the Principal Covariates Classification. Adapts PCovR for
+  classification tasks, proposed in [Jorgensen2025]_.
+* :ref:`KPCovR-api` the Kernel Principal Covariates Regression.
+  A kernel-based variation on the
   original PCovR method, proposed in [Helfrecht2020]_.
 """
 

src/skmatter/decomposition/_pcovc.py

@@ -100,7 +100,7 @@ class PCovC(LinearClassifierMixin, _BasePCov):
         default=`sample` when :math:`{n_{samples} < n_{features}}` and
         `feature` when :math:`{n_{features} < n_{samples}}`
 
-    classifier: estimator object or `precomputed`, default=None
+    classifier: `estimator object` or `precomputed`, default=None
         classifier for computing :math:`{\mathbf{Z}}`. The classifier should be one of
         `sklearn.linear_model.LogisticRegression`, `sklearn.linear_model.LogisticRegressionCV`,
         `sklearn.svm.LinearSVC`, `sklearn.discriminant_analysis.LinearDiscriminantAnalysis`,