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文件名称:Dimension Reduction A Guided Tour
文件大小:1.36MB
文件格式:PDF
更新时间:2021-09-24 15:18:55
Dimension Re Machine Lean
Dimension Reduction A Guided Tour
give a tutorial overview of several foundational 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
(CCA), kernel CCA, Fisher discriminant 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 eigenmaps, and spectral
clustering