RELATED TASKS

[philusnarh]

Multilabel classification

http://scikit-learn.org/stable/auto_examples/plot_multilabel.html#sphx-glr-auto-examples-plot-multilabel-py

A demo of K-Means clustering on the handwritten digits data http://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_digits.html#sphx-glr-auto-examples-cluster-plot-kmeans-digits-py

Faces dataset decompositions

http://scikit-learn.org/stable/auto_examples/decomposition/plot_faces_decomposition.html#sphx-glr-auto-examples-decomposition-plot-faces-decomposition-py

Concatenating multiple feature extraction methods

http://scikit-learn.org/stable/auto_examples/plot_feature_stacker.html#sphx-glr-auto-examples-plot-feature-stacker-py

FastICA on 2D point clouds http://scikit-learn.org/stable/auto_examples/decomposition/plot_ica_vs_pca.html#sphx-glr-auto-examples-decomposition-plot-ica-vs-pca-py

PCA example with Iris Data-set http://scikit-learn.org/stable/auto_examples/decomposition/plot_pca_iris.html#sphx-glr-auto-examples-decomposition-plot-pca-iris-py

Model selection with Probabilistic PCA and Factor Analysis (FA) http://scikit-learn.org/stable/auto_examples/decomposition/plot_pca_vs_fa_model_selection.html#sphx-glr-auto-examples-decomposition-plot-pca-vs-fa-model-selection-py

Kernel Density Estimation http://scikit-learn.org/stable/auto_examples/neighbors/plot_digits_kde_sampling.html#sphx-glr-auto-examples-neighbors-plot-digits-kde-sampling-py

Principal components analysis (PCA) http://scikit-learn.org/stable/auto_examples/decomposition/plot_pca_3d.html#sphx-glr-auto-examples-decomposition-plot-pca-3d-py

Kernel PCA http://scikit-learn.org/stable/auto_examples/decomposition/plot_kernel_pca.html#sphx-glr-auto-examples-decomposition-plot-kernel-pca-py

Blind source separation using FastICA

http://scikit-learn.org/stable/auto_examples/decomposition/plot_ica_blind_source_separation.html#sphx-glr-auto-examples-decomposition-plot-ica-blind-source-separation-py

sklearn.decomposition.PCA http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html

Bayesian Probabilistic Principle Component Analysis https://github.com/cangermueller/ppca

[philusnarh]

Linear Discriminant Analysis using Python (LDA) http://www.thejavageek.com/2018/04/30/linear-discriminant-analysis-using-python/ http://www.thejavageek.com/machine-learning/ http://www.thejavageek.com/2018/03/13/k-means-clustering/ http://www.thejavageek.com/2018/03/15/hierarchical-clustering-3/ http://www.thejavageek.com/2018/04/30/principal-component-analysis-using-python/ http://www.thejavageek.com/2018/02/27/k-nearest-neighbors-classification/ http://www.thejavageek.com/2018/02/28/svm-classification/

face rec

https://www.researchgate.net/publication/324975201_Face_recognition_system_based_on_principal_components_analysis_and_distance_measures

[philusnarh]

Denoise Stuff http://scikit-image.org/docs/dev/auto_examples/filters/plot_denoise.html

Zernike Package https://github.com/Sterncat/opticspy http://sterncat.github.io/files/Zernike_Polynomial_Method.html