Tumor Classification via Sparse Representation Based on Metasample
In this paper, we propose a new method for tumor classification using gene expression data. The new method expresses each testing sample as a linear combination of a set of metasamples extracted from all the training samples. Classification is achieved by a defined discriminating functions using the coefficient vector for the metasamples extracted from each category, which is obtained by l1-regularized least square. Since l1-normminimization could leads to sparse solution, our approach can be named as metasample based sparse representation classification (MSRC). The experimental results show that our method is efficient for tumor classification.