Bayesian Nonnegative Matrix Factorization with Stochastic Variational Inference

Authored by: Edoardo M. Airoldi , David M. Blei , Elena A. Erosheva , Stephen E. Fienberg , John Paisley , David M. Blei , Michael I. Jordan

Handbook of Mixed Membership Models and Their Applications

Print publication date:  November  2014
Online publication date:  November  2014

Print ISBN: 9781466504080
eBook ISBN: 9781466504097
Adobe ISBN:

10.1201/b17520-15

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Abstract

We present stochastic variational inference algorithms for two Bayesian nonnegative matrix factorization (NMF) models. These algorithms allow for fast processing of massive datasets. In particular, we derive stochastic algorithms for a Bayesian extension of the NMF algorithm of Lee and Seung (2001), and a matrix factorization model called correlated NMF, which is motivated by the correlated topic model (Blei and Lafferty, 2007). We apply our algorithms to roughly 1.8 million documents from the New York Times, comparing with online LDA (Hoffman et al., 2010b).

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