L1 Least Square for Cancer Diagnosis using Gene Expression Data
- *Corresponding Author:
- Dr. Fang-Xiang Wu
Divsion of Biomedical Engineering University of Saskatchewan,
Saskatoon, Saskatchewan, S7N 5A9, Canada,
E-mail : [email protected], [email protected]
Received date: March 19, 2009; Accepted date: April 27, 2009; Published date: April 27, 2009
Citation: Hang X, Wu FX (2009) L1 Least Square for Cancer Diagnosis using Gene Expression Data. J Comput Sci Syst Biol 2:167-173. doi:10.4172/jcsb.1000028
Copyright: © 2009 Hang X, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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.