An NMF-L-2,L-1-Norm Constraint Method for Characteristic Gene Selection

作者：Wang, Dong; Liu, Jin-Xing^*; Gao, Ying-Lian; Yu, Jiguo; Zheng, Chun-Hou; Xu, Yong

来源：PLos One, 2016, 11(7): e0158494.

DOI：10.1371/journal.pone.0158494

• NONNEGATIVE MATRIX FACTORIZATION
• EXPRESSION DATA
• CORE GENES
• NOISE

Recent research has demonstrated that characteristic gene selection based on gene expression data remains faced with considerable challenges. This is primarily because gene expression data are typically high dimensional, negative, non-sparse and noisy. However, existing methods for data analysis are able to cope with only some of these challenges. In this paper, we address all of these challenges with a unified method: nonnegative matrix factorization via the L-2,L-1-norm (NMF-L-2,L-1). While L-2,L-1-norm minimization is applied to both the error function and the regularization term, our method is robust to outliers and noise in the data and generates sparse results. The application of our method to plant and tumor gene expression data demonstrates that NMF-L-2,L-1 can extract more characteristic genes than other existing state-of-the-art methods.

• 出版日期2016-7-18
• 单位哈尔滨工业大学深圳研究生院; 曲阜师范大学