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Learning a Weighted Meta-Sample Based Parameter Free Sparse Representation Classification for Microarray Data

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  • Bo Liao
  • Yan Jiang
  • Guanqun Yuan
  • Wen Zhu
  • Lijun Cai
  • Zhi Cao

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 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 regularization of underdetermined linear equations. Thus, data dependent sparsity can be adaptively tuned. A simple characteristic function is eventually utilized to achieve classification. Asymptotic time complexity analysis is applied to our method. Compared with some state-of-the-art classifiers, the proposed method has lower time complexity and more flexibility. Experiments on eight samples of publicly available gene expression profile data show the effectiveness of the proposed method.

Suggested Citation

  • Bo Liao & Yan Jiang & Guanqun Yuan & Wen Zhu & Lijun Cai & Zhi Cao, 2014. "Learning a Weighted Meta-Sample Based Parameter Free Sparse Representation Classification for Microarray Data," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-12, August.
  • Handle: RePEc:plo:pone00:0104314
    DOI: 10.1371/journal.pone.0104314
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