Cancer Classification by Sparse Representation using Microarray Gene Expression Data

被引：7
|
作者：
Hang, Xiyi [1 ]
机构：
[1] Calif State Univ Northridge, Dept Elect & Comp Engn, Northridge, CA 91330 USA
来源：
2008 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE WORKSHOPS, PROCEEDINGS | 2008年
关键词：
D O I：
10.1109/BIBMW.2008.4686232
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
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 l(1)-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.
引用
收藏
页码：174 / 177
页数：4
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