查看文章

Current nonnegative matrix factorization (NMF) deals with X = FG^T type. We provide a systematic analysis and extensions of NMF to the symmetric W = HH^T, and the weighted W = HSH^T. We show that (1) W = HH^T is equivalent to Kernel if-means clustering and the Laplacian-based spectral clustering. (2) X = FG^T is equivalent to simultaneous clustering of rows and columns of a bipartite graph. Algorithms are given for computing these symmetric NMFs.