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scikit-learn 1.3.2
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# Computation times

00:36.099 total execution time for auto_examples_decomposition files:

Image denoising using dictionary learning (`plot_image_denoising.py`) | 00:23.801 | 0.0 MB
Faces dataset decompositions (`plot_faces_decomposition.py`) | 00:06.802 | 0.0 MB
Model selection with Probabilistic PCA and Factor Analysis (FA) (`plot_pca_vs_fa_model_selection.py`) | 00:03.660 | 0.0 MB
Kernel PCA (`plot_kernel_pca.py`) | 00:00.428 | 0.0 MB
Factor Analysis (with rotation) to visualize patterns (`plot_varimax_fa.py`) | 00:00.277 | 0.0 MB
Blind source separation using FastICA (`plot_ica_blind_source_separation.py`) | 00:00.224 | 0.0 MB
Sparse coding with a precomputed dictionary (`plot_sparse_coding.py`) | 00:00.216 | 0.0 MB
FastICA on 2D point clouds (`plot_ica_vs_pca.py`) | 00:00.214 | 0.0 MB
Incremental PCA (`plot_incremental_pca.py`) | 00:00.158 | 0.0 MB
Principal components analysis (PCA) (`plot_pca_3d.py`) | 00:00.138 | 0.0 MB
Comparison of LDA and PCA 2D projection of Iris dataset (`plot_pca_vs_lda.py`) | 00:00.128 | 0.0 MB
PCA example with Iris Data-set (`plot_pca_iris.py`) | 00:00.054 | 0.0 MB