Weighted Nonnegative Matrix Tri-Factorization for Co-clustering

Abstract

Nonnegative matrix tri-factorization and spectral co-clustering are two popular techniques that allow simultaneous clustering of the rows and columns of a matrix. In this paper, by adding a weighting scheme derived from spectral co-clustering into the objective function of nonnegative matrix tri-factorization, we show that the normalized cut information for co-clustering can be incorporated into nonnegative matrix tri-factorization. With the weighting scheme, a weighted nonnegative matrix tri-factorization algorithm for co-clustering is proposed, and extensive experiments show that our method statistically outperforms state-of-the-art co-clustering algorithms.