Learning a weighted meta-sample based parameter free sparse representation classification for microarray data.

ABSTRACT: Sparse representation classification (SRC) is one of the most promising classification methods for supervised learning. This method can effectively exploit discriminating information by introducing a [Symbol: see text]1 regularization terms to the data. With the desirable property of sparisty, SRC is robust to both noise and outliers. In this study, we propose a weighted meta-sample based non-parametric sparse representation classification method for the accurate identification of tumor subtype. The proposed method includes three steps. First, we extract the weighted meta-samples for each sub class from raw data, and the rationality of the weighting strategy is proven mathematically. Second, sparse representation coefficients can be obtained by [Symnbol: see text]1 regularization of underd

SUBMITTER: Liao B 

PROVIDER: S-EPMC4130588 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature