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• W2117920736 endingPage "364" @default.
• W2117920736 startingPage "275" @default.
• W2117920736 abstract "We give a tutorial overview of several geometric methods for dimension reduction. We divide the methods into projective methods and methods that model the manifold on which the data lies. For projective methods, we review projection pursuit, principal component analysis (PCA), kernel PCA, probabilistic PCA, canonical correlation analysis, oriented PCA, and several techniques for sufficient dimension reduction. For the manifold methods, we review multidimensional scaling (MDS), landmark MDS, Isomap, locally linear embedding, Laplacian" @default.
• W2117920736 created "2016-06-24" @default.
• W2117920736 creator A5034285513 @default.
• W2117920736 date "2009-01-01" @default.
• W2117920736 modified "2023-10-06" @default.
• W2117920736 title "Dimension Reduction: A Guided Tour" @default.
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• W2117920736 doi "https://doi.org/10.1561/2200000002" @default.

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