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Results tagged with dimensionality-reduction
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user 36915
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.
5
votes
How to prove that the manifold assumption is correct?
Any finite set of points can fit on any manifold (theorem reference needed, I cant remember what the theorem is, I just remember this fact from uni).
If one does not want all points to be identified …
- 933
7
votes
When is it appropriate to use PCA as a preprocessing step?
Using PCA for feature selection (removing non-predictive features) is an extremely expensive way to do it. PCA algos are often O(n^3). Rather a much better and more efficient approach would be to us …
- 933