Cancer classification by sparse representation using microarray gene expression data

In this paper, a new method is proposed for cancer diagnosis using gene expression data by casting the classification problem as finding sparse representations of test samples with respect to training samples. The sparse representation is efficiently computed by lscr₁-regularized least square. Numerical experiment shows that the new approach can match the best performance achieved by support vector machines (SVM). Sparse representation approach also has no need of model selection.