diff --git a/doc/unsupervised.rst b/doc/unsupervised.rst
index 2840f98..68c9d73 100644
--- a/doc/unsupervised.rst
+++ b/doc/unsupervised.rst
@@ -7,10 +7,15 @@ Unsupervised feature selection
 ==============================
 
 We can use :class:`FastCan` to do unsupervised feature selection.
-The unsupervised application of :class:`FastCan` tries to select features, which
-maximize the sum of the squared canonical correlation (SSC) with the principal
-components (PCs) acquired from PCA (principal component analysis) of the feature
-matrix :math:`X`. See the example below.
+The basic idea of unsupervised feature selection is to use the learned features,
+like PCA (principal component analysis), or the hand-crafted features, like Fourier
+transform, as the targets and to select the features which are most correlated with
+the targets.
+
+In PCA cases, the unsupervised application of :class:`FastCan` tries to select
+features, which maximize the sum of the squared canonical correlation (SSC) with
+the principal components (PCs) acquired from PCA of the feature matrix :math:`X` [1]_.
+See the example below.
 
     >>> from sklearn.decomposition import PCA
     >>> from sklearn import datasets
@@ -29,10 +34,22 @@ matrix :math:`X`. See the example below.
     Because :class:`FastCan` selects features in a greedy manner, which may lead to
     suboptimal results.
 
+However, PCA does not take nonlinearity into consideration.
+To solve the problem, targets (learned features) can be generated by manifold
+learning [2]_.
+Then, use :class:`FastCan` to select features, which is the same as the above.
+
+
 .. rubric:: References
 
-* `"Automatic Selection of Optimal Structures for Population-Based
-  Structural Health Monitoring" <https://doi.org/10.1007/978-3-031-34946-1_10>`_
-  Wang, T., Worden, K., Wagg, D.J., Cross, E.J., Maguire, A.E., Lin, W.
-  In: Conference Proceedings of the Society for Experimental Mechanics Series.
-  Springer, Cham. (2023).
\ No newline at end of file
+.. [1] `"Automatic Selection of Optimal Structures for Population-Based
+    Structural Health Monitoring" <https://doi.org/10.1007/978-3-031-34946-1_10>`_
+    Wang, T., Worden, K., Wagg, D.J., Cross, E.J., Maguire, A.E., & Lin, W.
+    In: Conference Proceedings of the Society for Experimental Mechanics Series.
+    Springer, Cham. (2023).
+
+.. [2] `"Manifold learning-based unsupervised feature selection for structural health
+    monitoring" <https://mrforum.com/wp-content/uploads/open_access/9781644903513/9.pdf>`_
+    Wang, T., & Sun, L.
+    Materials Research Proceedings, 50, 82-89, (2025).
+