Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/14928
Title: Metasample-based sparse representation for tumor classification
Authors: Zheng, CH
Zhang, L 
Ng, TY 
Shiu, SCK 
Huang, DS
Keywords: Gene expression data
Metasample
Sparse representation
Tumors classification
Issue Date: 2011
Publisher: ACM Special Interest Group
Source: IEEE/ACM transactions on computational biology and bioinformatics, 2011, v. 8, no. 5, 5708133, p. 1273-1282 How to cite?
Journal: IEEE/ACM transactions on computational biology and bioinformatics 
Abstract: A reliable and accurate identification of the type of tumors is crucial to the proper treatment of cancers. In recent years, it has been shown that sparse representation (SR) by l 1-norm minimization is robust to noise, outliers and even incomplete measurements, and SR has been successfully used for classification. This paper presents a new SR-based method for tumor classification using gene expression data. A set of metasamples are extracted from the training samples, and then an input testing sample is represented as the linear combination of these metasamples by l 1-regularized least square method. Classification is achieved by using a discriminating function defined on the representation coefficients. Since l 1-norm minimization leads to a sparse solution, the proposed method is called metasample-based SR classification (MSRC). Extensive experiments on publicly available gene expression data sets show that MSRC is efficient for tumor classification, achieving higher accuracy than many existing representative schemes.
URI: http://hdl.handle.net/10397/14928
ISSN: 1545-5963
EISSN: 1557-9964
DOI: 10.1109/TCBB.2011.20
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