From b54929585f29fc775111ba2d6a06b87e530ec7e8 Mon Sep 17 00:00:00 2001 From: sikai zhang Date: Tue, 19 Aug 2025 11:46:32 +0800 Subject: [PATCH 1/2] DOC add new reference for unsupervised feature selection tutorial --- doc/unsupervised.rst | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/doc/unsupervised.rst b/doc/unsupervised.rst index 2840f98..36daba0 100644 --- a/doc/unsupervised.rst +++ b/doc/unsupervised.rst @@ -31,8 +31,13 @@ matrix :math:`X`. See the example below. .. rubric:: References +* `"Manifold learning-based unsupervised feature selection for structural health + monitoring" `_ + Wang, T., & Sun, L. + Materials Research Proceedings, 50, 82-89, (2025). + * `"Automatic Selection of Optimal Structures for Population-Based Structural Health Monitoring" `_ - Wang, T., Worden, K., Wagg, D.J., Cross, E.J., Maguire, A.E., Lin, W. + 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 From f2546e3713189312d7cf09b3dd543ad2156f37c0 Mon Sep 17 00:00:00 2001 From: sikai zhang Date: Tue, 19 Aug 2025 13:09:54 +0800 Subject: [PATCH 2/2] add illustration --- doc/unsupervised.rst | 38 +++++++++++++++++++++++++------------- 1 file changed, 25 insertions(+), 13 deletions(-) diff --git a/doc/unsupervised.rst b/doc/unsupervised.rst index 36daba0..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 @@ -29,15 +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 -* `"Manifold learning-based unsupervised feature selection for structural health - monitoring" `_ - Wang, T., & Sun, L. - Materials Research Proceedings, 50, 82-89, (2025). +.. [1] `"Automatic Selection of Optimal Structures for Population-Based + Structural Health Monitoring" `_ + 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" `_ + Wang, T., & Sun, L. + Materials Research Proceedings, 50, 82-89, (2025). -* `"Automatic Selection of Optimal Structures for Population-Based - Structural Health Monitoring" `_ - 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