Gene expression data clustering based on graph regularized subspace segmentation

Gene expression data clustering offers a powerful approach to detect cancers. Specifically, gene expression data clustering based on nonnegative matrix factorization (NMF) has been widely applied to identify tumors. However, traditional NMF methods cannot deal with negative data and easily lead to local optimum because the iterative methods are adopted to solve the optimal problem. To avoid these problems of NMF methods, we propose graph regularized subspace segmentation method (GRSS) for clustering gene expression data. The global optimal solution of GRSS can be achieved by solving a Sylvester equation. Experimental results on eight gene expression data sets show that GRSS has significant performance improvement compared with other subspace segmentation methods, traditional clustering methods and various extensions of NMF.