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- Q6664553 subject Q6069331.
- Q6664553 subject Q6960426.
- Q6664553 abstract "Local tangent space alignment (LTSA) is a method for manifold learning, which can efficiently learn a nonlinear embedding into low-dimensional coordinates from high-dimensional data, and can also reconstruct high-dimensional coordinates from embedding coordinates. It is based on the intuition that when a manifold is correctly unfolded, all of the tangent hyperplanes to the manifold will become aligned. It begins by computing the k-nearest neighbors of every point. It computes the tangent space at every point by computing the d-first principal components in each local neighborhood. It then optimizes to find an embedding that aligns the tangent spaces, but it ignores the label information conveyed by data samples, and thus can not be used for classification directly.".
- Q6664553 wikiPageWikiLink Q131251.
- Q6664553 wikiPageWikiLink Q203920.
- Q6664553 wikiPageWikiLink Q4440864.
- Q6664553 wikiPageWikiLink Q49906.
- Q6664553 wikiPageWikiLink Q6069331.
- Q6664553 wikiPageWikiLink Q657586.
- Q6664553 wikiPageWikiLink Q660848.
- Q6664553 wikiPageWikiLink Q6960426.
- Q6664553 wikiPageWikiLink Q7049464.
- Q6664553 wikiPageWikiLink Q909601.
- Q6664553 comment "Local tangent space alignment (LTSA) is a method for manifold learning, which can efficiently learn a nonlinear embedding into low-dimensional coordinates from high-dimensional data, and can also reconstruct high-dimensional coordinates from embedding coordinates. It is based on the intuition that when a manifold is correctly unfolded, all of the tangent hyperplanes to the manifold will become aligned. It begins by computing the k-nearest neighbors of every point.".
- Q6664553 label "Local tangent space alignment".