Integrating Symmetric Nonnegative Matrix Factorization and Normalized Cut Spectral Clustering

In this paper, we integrate symmetric NMF and normalized cut into a single clustering framework and derive the computational algorithm. Another contribution is to provide a new matrix inequality which is useful for the analysis of 4-th order matrix polynomials. We perform experiments on three real-life data sets to show the effectiveness of the proposed algorithm. We also demonstrate the importance of the orthogonality among matrix factors.