Infinite Positive Semidefinite Tensor Factorization for Source Separation of Mixture Signals


Kazuyoshi Yoshii, Ryota Tomioka, Daichi Mochihashi, Masataka Goto ;
Proceedings of the 30th International Conference on Machine Learning, PMLR 28(3):576-584, 2013.


This paper presents a new class of tensor factorization called positive semidefinite tensor factorization (PSDTF) that decomposes a set of positive semidefinite (PSD) matrices into the convex combinations of fewer PSD basis matrices. PSDTF can be viewed as a natural extension of nonnegative matrix factorization. One of the main problems of PSDTF is that an appropriate number of bases should be given in advance. To solve this problem, we propose a nonparametric Bayesian model based on a gamma process that can instantiate only a limited number of necessary bases from the infinitely many bases assumed to exist. We derive a variational Bayesian algorithm for closed-form posterior inference and a multiplicative update rule for maximum-likelihood estimation. We evaluated PSDTF on both synthetic data and real music recordings to show its superiority.

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