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Techniques for reducing a large number of variables or dimensions spanned by data to a smaller number of dimensions while preserving as much information about the data as possible. Prominent methods include PCA, Factor Analysis, MDS, Independent Component Analysis, Multiple Correspondence Analysis, Isomap, etc. The two main subclasses of techniques: feature extraction and feature selection.

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How to plot High Dimensional supervised K-means on a 2D plot chart

You can try using T-SNE https://lvdmaaten.github.io/tsne/ "which is a technique for dimensionality reduction that is particularly well suited for the visualization of high-dimensional datasets" It …
Denis Gordeev's user avatar