ENG  Vol.5 No.5 B , May 2013
Metasample-Based Robust Sparse Representation for Tumor Classification
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.

Cite this paper
B. Gan, C.-H. Zheng and J.-X. Liu, "Metasample-Based Robust Sparse Representation for Tumor Classification," Engineering, Vol. 5 No. 5B, 2013, pp. 78-83. doi: 10.4236/eng.2013.55B016.
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