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BioMed Research International
Volume 2014 (2014), Article ID 420856, 7 pages
Research Article

Sparse Representation for Tumor Classification Based on Feature Extraction Using Latent Low-Rank Representation

1College of Information and Communication Technology, Qufu Normal University, Rizhao 276800, China
2College of Electrical Engineering and Automation, Anhui University, Hefei 230000, China
3Intelligent Computing Lab, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230000, China

Received 13 November 2013; Revised 27 December 2013; Accepted 27 December 2013; Published 11 February 2014

Academic Editor: Xing-Ming Zhao

Copyright © 2014 Bin Gan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.