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- W1966347620 abstract "We interpret several well-known algorithms for dimensionality reduction of manifolds as kernel methods. Isomap, graph Laplacian eigenmap, and locally linear embedding (LLE) all utilize local neighborhood information to construct a global embedding of the manifold. We show how all three algorithms can be described as kernel PCA on specially constructed Gram matrices, and illustrate the similarities and differences between the algorithms with representative examples." @default.
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- W1966347620 date "2004-01-01" @default.
- W1966347620 modified "2023-10-11" @default.
- W1966347620 title "A kernel view of the dimensionality reduction of manifolds" @default.
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- W1966347620 doi "https://doi.org/10.1145/1015330.1015417" @default.
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