Metasample-Based Robust Sparse Representation for Tumor Classification

DOI: 10.4236/eng.2013.55B016   PDF   HTML     3,200 Downloads   4,485 Views   Citations


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.

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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.

Conflicts of Interest

The authors declare no conflicts of interest.


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