Geometric Methods for Feature Selection and Dimensional Reduction
- Chris J.C. Burges
Data Mining and Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers |
Published by Kluwer Academic
We give a tutorial overview† of several geometric methods for feature selection and dimensional 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, and oriented PCA; and for the manifold methods, we review multidimensional scaling (MDS), landmark MDS, Isomap, locally linear embedding, Laplacian eigenmaps and spectral clustering. The Nystro¨m method, which links several of the algorithms, is also reviewed. The goal is to provide a self-contained review of the concepts and mathematics underlying these algorithms.