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t-SNE and UMAP
t-SNE and UMAP
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Intermediate
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Which comparison between PCA, LDA, NMF, t-SNE, and UMAP is accurate?
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A.
They answer different objectives: PCA variance, LDA labels, NMF nonnegative parts, and t-SNE/UMAP local neighborhoods.
B.
They all solve the same covariance eigenvector problem, just with different plotting defaults and color maps.
C.
t-SNE and UMAP preserve all global distances exactly, unlike PCA.
D.
LDA ignores labels, while PCA is the supervised class-separation method.
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