title: Sparse representation for tumor classification based on feature extraction using latent low-rank representation.
abstract: Accurate tumor classification is crucial to the proper treatment of cancer. To now, sparse representation (SR) has shown its great performance for tumor classification. This paper conceives a new SR-based method for tumor classification by using gene expression data. In the proposed method, we firstly use latent low-rank representation for extracting salient features and removing noise from the original samples data. Then we use sparse representation classifier (SRC) to build tumor classification model. The experimental results on several real-world data sets show that our method is more efficient and more effective than the previous classification methods including SVM, SRC, and LASSO.
authors.affiliation: College of Information and Communication Technology, Qufu Normal University, Rizhao 276800, China.
authors.forename: Bin
authors.identifier: 0000-0001-5489-663X
authors.initials: B
authors.lastname: Gan
authors.affiliation: College of Information and Communication Technology, Qufu Normal University, Rizhao 276800, China ; College of Electrical Engineering and Automation, Anhui University, Hefei 230000, China.
authors.forename: Chun-Hou
authors.initials: CH
authors.lastname: Zheng
authors.affiliation: College of Electrical Engineering and Automation, Anhui University, Hefei 230000, China.
authors.forename: Jun
authors.initials: J
authors.lastname: Zhang
authors.affiliation: Intelligent Computing Lab, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230000, China.
authors.forename: Hong-Qiang
authors.initials: HQ
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D004273:DNA, Neoplasm / Q000145:classification; D019992:Databases as Topic; D052138:Genes, Neoplasm; D006801:Humans; D009369:Neoplasms / Q000145:classification* / Q000235:genetics
chemical_list: D004273:DNA, Neoplasm