L1 Least Square for Cancer Diagnosis Using Gene Expression Data |
Xiyi Hang1,FangXiang Wu2,3,* |
| 1Department of Electrical and Computer Engineering,
California State University, Northridge, CA 91330, USA |
| 2Department of Mechanical Engineering |
| 3Divsion of Biomedical Engineering , University of Saskatchewan,
Saskatoon, Saskatchewan, S7N 5A9, Canada |
| *Corresponding author: |
Dr. Fang-Xiang Wu, Divsion of Biomedical Engineering
University of Saskatchewan, Saskatoon, Saskatchewan, S7N 5A9,
Canada,
E-mail: xhang@csun.edu, faw341@mail.usask.ca |
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| Received March 19, 2008; Accepted April 27, 2009; Published 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: ©2008 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. |
| Abstract |
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. |
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