Sparse partial least squares classification for high dimensional data.

ABSTRACT: Partial least squares (PLS) is a well known dimension reduction method which has been recently adapted for high dimensional classification problems in genome biology. We develop sparse versions of the recently proposed two PLS-based classification methods using sparse partial least squares (SPLS). These sparse versions aim to achieve variable selection and dimension reduction simultaneously. We consider both binary and multicategory classification. We provide analytical and simulation-based insights about the variable selection properties of these approaches and benchmark them on well known publicly available datasets that involve tumor classification with high dimensional gene expression data. We show that incorporation of SPLS into a generalized linear model (GLM) framework provides high

SUBMITTER: Chung D 

PROVIDER: S-EPMC2861314 | biostudies-literature | 2010

REPOSITORIES: biostudies-literature