title: Maxdenominator Reweighted Sparse Representation for Tumor Classification.
abstract: The classification of tumors is crucial for the proper treatment of cancer. Sparse representation-based classifier (SRC) exhibits good classification performance and has been successfully used to classify tumors using gene expression profile data. In this study, we propose a three-step maxdenominator reweighted sparse representation classification (MRSRC) method to classify tumors. First, we extract a set of metagenes from the training samples. These metagenes can capture the structures inherent to the data and are more effective for classification than the original gene expression data. Second, we use a reweighted regularization method to obtain the sparse representation coefficients. Reweighted regularization can enhance sparsity and obtain better sparse representation coefficients. Third, we classify the data by utilizing a maxdenominator residual error function. Maxdenominator strategy can reduce the residual error and improve the accuracy of the final classification. Extensive experiments using publicly available gene expression profile data sets show that the performance of MRSRC is comparable with or better than many existing representative methods.
authors.affiliation: College of Information Science and Engineering, Hunan University, Changsha, Hunan 410082, China.
authors.forename: Weibiao
authors.initials: W
authors.lastname: Li
authors.affiliation: College of Information Science and Engineering, Hunan University, Changsha, Hunan 410082, China.
authors.forename: Bo
authors.initials: B
authors.lastname: Liao
authors.affiliation: College of Information Science and Engineering, Hunan University, Changsha, Hunan 410082, China.
authors.forename: Wen
authors.initials: W
authors.lastname: Zhu
authors.affiliation: College of Information Science and Engineering, Hunan University, Changsha, Hunan 410082, China.
authors.forename: Min
authors.initials: M
authors.lastname: Chen
authors.affiliation: College of Information Science and Engineering, Hunan University, Changsha, Hunan 410082, China.
authors.forename: Li
authors.initials: L
authors.lastname: Peng
authors.affiliation: College of Information Science and Engineering, Hunan University, Changsha, Hunan 410082, China.
authors.forename: Xiaohui
authors.initials: X
authors.lastname: Wei
authors.affiliation: College of Information Science and Engineering, Hunan University, Changsha, Hunan 410082, China.
authors.forename: Changlong
authors.initials: C
authors.lastname: Gu
authors.affiliation: Department of Computer Science, State University of New York, New Paltz, New York 12561, USA.
authors.forename: Keqin
authors.initials: K
authors.lastname: Li
mesh_terms: D000465:Algorithms; D019992:Databases as Topic; D052138:Genes, Neoplasm; D006801:Humans; D009369:Neoplasms / Q000145:classification* / Q000235:genetics