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L1 Least Square for Cancer Diagnosis using Gene Expression Data

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Author(s): Xiyi Hang | Fang-Xiang Wu

Journal: Journal of Computer Science & Systems Biology
ISSN 0974-7230

Volume: 02;
Issue: 02;
Start page: 167;
Date: 2009;
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Keywords: l1-norm minimization | least square regression | classification | cancer | gene expression data | support vector machine

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, andhence 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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