Metasample-Based Robust Sparse Representation for Tumor Classification

Bin Gan, Chun-Hou Zheng, Jin-Xing Liu

College of Information technology and communication, Qufu Normal University, Rizhao, China.
College of Information technology and communication, Qufu Normal University, Rizhao, China;College of Electrical Engineering and Automation, Anhui University, Hefei, China.

DOI: 10.4236/eng.2013.55B016                

Abstract

In this paper, based on sparse representation classification and robust thought, we propose a new classifier, named MRSRC (Metasample Based Robust Sparse Representation Classificatier), for DNA microarray data classification. Firstly, we extract Metasample from trainning sample. Secondly, a weighted matrix W is added to solve an l1-regular- ized least square problem. Finally, the testing sample is classified according to the sparsity coefficient vector of it. The experimental results on the DNA microarray data classification prove that the proposed algorithm is efficient.

Keywords

DNA Microarray Data; Sparse Representation Classification; MRSRC; Robust

Conflicts of Interest

The authors declare no conflicts of interest.

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