文件名称：Dimension Reduction：A Guided Tour
文件大小：443KB
文件格式：PDF
更新时间：2013-12-01 15:32:14
Dimension Reduction
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 eigenmaps and spectral clustering. The Nystr¨om method, which links several of the manifold algorithms, is also reviewed. The goal is to provide a self-contained overview of key concepts underlying many of these algorithms, and to give pointers for further reading.