title: 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 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.
authors.affiliation: Key Laboratory for Embedded and Network Computing of Hunan Province, the College of Information Science and Engineering, Hunan University, Changsha Hunan, China.
authors.forename: Bo
authors.initials: B
authors.lastname: Liao
authors.affiliation: Key Laboratory for Embedded and Network Computing of Hunan Province, the College of Information Science and Engineering, Hunan University, Changsha Hunan, China.
authors.forename: Yan
authors.initials: Y
authors.lastname: Jiang
authors.affiliation: Key Laboratory for Embedded and Network Computing of Hunan Province, the College of Information Science and Engineering, Hunan University, Changsha Hunan, China.
authors.forename: Guanqun
authors.initials: G
authors.lastname: Yuan
authors.affiliation: Key Laboratory for Embedded and Network Computing of Hunan Province, the College of Information Science and Engineering, Hunan University, Changsha Hunan, China.
authors.forename: Wen
authors.initials: W
authors.lastname: Zhu
authors.affiliation: Key Laboratory for Embedded and Network Computing of Hunan Province, the College of Information Science and Engineering, Hunan University, Changsha Hunan, China.
authors.forename: Lijun
authors.initials: L
authors.lastname: Cai
authors.affiliation: Key Laboratory for Embedded and Network Computing of Hunan Province, the College of Information Science and Engineering, Hunan University, Changsha Hunan, China.
authors.forename: Zhi
authors.initials: Z
authors.lastname: Cao
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D057225:Data Mining; D066264:Datasets as Topic; D020869:Gene Expression Profiling; D006801:Humans; D015233:Models, Statistical; D009369:Neoplasms / Q000175:diagnosis / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis; D015203:Reproducibility of Results