Journal of Computer Science & Systems Biology

L1 Least Square for Cancer Diagnosis using Gene Expression Data

Abstract

Xiyi Hang and Fang-Xiang Wu

The performance of most methods for cancer diagnosis using gene expression data greatly depends on careful model selection. Least square for classification has no need of model selection. However, a major drawback prevents it from successful application in microarray data classification: lack of robustness to outliers. In this paper we cast linear regression as a constrained l1-norm minimization problem to greatly alleviate its sensitivity to outliers, and hence the name l1 least square. The numerical experiment shows that l1 least square can match the best performance achieved by support vector machines (SVMs) with careful model selection.