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BioMed Research International
Volume 2017 (2017), Article ID 1096028, 8 pages
https://doi.org/10.1155/2017/1096028
Research Article

Gene Feature Extraction Based on Nonnegative Dual Graph Regularized Latent Low-Rank Representation

School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, China

Correspondence should be addressed to Guoliang Yang and Zhengwei Hu

Received 21 January 2017; Accepted 13 March 2017; Published 30 March 2017

Academic Editor: Gang Liu

Copyright © 2017 Guoliang Yang and Zhengwei Hu. 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.

Abstract

Aiming at the problem of gene expression profile’s high redundancy and heavy noise, a new feature extraction model based on nonnegative dual graph regularized latent low-rank representation (NNDGLLRR) is presented on the basis of latent low-rank representation (Lat-LRR). By introducing dual graph manifold regularized constraint, the NNDGLLRR can keep the internal spatial structure of the original data effectively and improve the final clustering accuracy while segmenting the subspace. The introduction of nonnegative constraints makes the computation with some sparsity, which enhances the robustness of the algorithm. Different from Lat-LRR, a new solution model is adopted to simplify the computational complexity. The experimental results show that the proposed algorithm has good feature extraction performance for the heavy redundancy and noise gene expression profile, which, compared with LRR and Lat-LRR, can achieve better clustering accuracy.