Combining graph regularization with nonnegative matrix (tri-)factorization (NMF) has shown great performance improvement compared with traditional nonnegativematrix (tri-)factorizationmodels due to its ability to utilize the geometric structure of the documents and words. In this paper, we show that these models are not well-defined and suffering from trivial solution and scale transfer problem...

NonnegativeMatrix Factorization (NMF) based coclustering methods have attracted increasing attention in recent years because of their mathematical elegance and encouraging empirical results. However, the algorithms to solve NMF problems usually involve intensive matrix multiplications, which make them computationally inefficient. In this paper, instead of constraining the factor matrices of NMF...

We review algorithms developed for nonnegativematrix factorization (NMF) and 1 nonnegative tensor factorization (NTF) from a unified view based on the block coordinate 2 descent (BCD) framework. NMF and NTF are low-rank approximation methods for matri3 ces and tensors in which the low-rank factors are constrained to have only nonnegative 4 elements. The nonnegativity constraints have been shown...