Recent research shows that characteristic gene selection based on gene expression data remains faced with considerable challenges. This is primarily because vast amount of gene expression data have been generated with the development of gene detection technology. Nonetheless, the recognition rate and reliability of gene selection still need to be improved. In this paper, we propose a novel constrained method: robust graph regularized discriminative nonnegative matrix factorization (RGDNMF) for characteristic gene selection. The method mainly includes two aspects: firstly, we incorporate both intrinsic geometrical structure and discriminative label information into the NMF model. Secondly, we adopt L2,1 -norm minimization to both the error function and the regularization term which is robust to noises and outliers in gene data. Furthermore we present the multiplicative update rules and the convergence proof. Our experiments demonstrate that RGDNMF is far more effective than other existing methods.
['Ling-Yun Dai', 'Chun-Mei Feng', 'Jin-Xing Liu', 'Chun-Hou Zheng', 'Mi-Xiao Hou', 'Jiguo Yu']
Robust graph regularized discriminative nonnegative matrix factorization for characteristic gene selection