Probability-enhanced sufficient dimension reduction for binary classification.

ABSTRACT: In high-dimensional data analysis, it is of primary interest to reduce the data dimensionality without loss of information. Sufficient dimension reduction (SDR) arises in this context, and many successful SDR methods have been developed since the introduction of sliced inverse regression (SIR) [Li (1991) Journal of the American Statistical Association 86, 316-327]. Despite their fast progress, though, most existing methods target on regression problems with a continuous response. For binary classification problems, SIR suffers the limitation of estimating at most one direction since only two slices are available. In this article, we develop a new and flexible probability-enhanced SDR method for binary classification problems by using the weighted support vector machine (WSVM). The key idea

SUBMITTER: Shin SJ 

PROVIDER: S-EPMC4670268 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature