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Results tagged with dimensionality-reduction
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user 213793
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 does UMAP deals with the curse of dimensionality?
This question is related to this one which was not answered.
The curse of dimensionality states that in high dimension every distance between pairs of points tends to be the same. See this answer for …
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