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Biomed Res Int. 2014;2014:420856. doi: 10.1155/2014/420856. Epub 2014 Feb 11.

Sparse representation for tumor classification based on feature extraction using latent low-rank representation.

Author information

1
College of Information and Communication Technology, Qufu Normal University, Rizhao 276800, China.
2
College of Information and Communication Technology, Qufu Normal University, Rizhao 276800, China ; College of Electrical Engineering and Automation, Anhui University, Hefei 230000, China.
3
College of Electrical Engineering and Automation, Anhui University, Hefei 230000, China.
4
Intelligent Computing Lab, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230000, China.

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.

PMID:
24678505
PMCID:
PMC3942202
DOI:
10.1155/2014/420856
[Indexed for MEDLINE]
Free PMC Article
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