Automatic Rank Determination in Projective Nonnegative Matrix Factorization

Zhirong Yang, Zhanxing Zhu, Erkki Oja

Abstract: Projective Nonnegative Matrix Factorization (PNMF) has demonstrated advantages in both sparse feature extraction and clustering. However, PNMF requires users to specify the column rank of the approximative projection matrix, the value of which is unknown beforehand. In this paper, we propose a method called ARDPNMF to automatically determine the column rank in PNMF. Our method is based on automatic relevance determination (ARD) with Jeffrey’s prior. After deriving the multiplicative update rule using the expectation-maximization technique for ARDPNMF, we test it on various synthetic and real-world datasets for feature extraction and clustering applications to show the effectiveness of our algorithm. For FERET faces and the Swimmer dataset, interpretable number of features are obtained correctly via our algorithm. Several UCI datasets for clustering are also tested, in which we find that ARDPNMF can estimate the number of clusters quite accurately with low deviation and good cluster purity.