ONP-MF: An Orthogonal Nonnegative Matrix Factorization Algorithm with Application to Clustering

Abstract. Given a nonnegative matrix M, the orthogonal nonnegative matrix factorization (ONMF) problem consists in finding a nonnegative matrix U and an orthogonal nonnegative matrix V such that the product UV is as close as possible to M. The importance of ONMF comes from its tight connection with data clustering. In this paper, we propose a new ONMF method, called ONP-MF, and we show that it performs in aver-age better than other ONMF algorithms in terms of accuracy on several datasets in text clustering and hyperspectral unmixing.