Particle filtering for nonparametric Bayesian matrix factorization

Research output: Chapter in Book/Report/Conference proceedingConference contribution

28 Scopus citations

Abstract

Many unsupervised learning problems can be expressed as a form of matrix factorization, reconstructing an observed data matrix as the product of two matrices of latent variables. A standard challenge in solving these problems is determining the dimensionality of the latent matrices. Nonparametric Bayesian matrix factorization is one way of dealing with this challenge, yielding a posterior distribution over possible factorizations of unbounded dimensionality. A drawback to this approach is that posterior estimation is typically done using Gibbs sampling, which can be slow for large problems and when conjugate priors cannot be used. As an alternative, we present a particle filter for posterior estimation in nonparametric Bayesian matrix factorization models. We illustrate this approach with two matrix factorization models and show favorable performance relative to Gibbs sampling.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 19 - Proceedings of the 2006 Conference
Pages1513-1520
Number of pages8
StatePublished - Dec 1 2007
Externally publishedYes
Event20th Annual Conference on Neural Information Processing Systems, NIPS 2006 - Vancouver, BC, Canada
Duration: Dec 4 2006Dec 7 2006

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258

Other

Other20th Annual Conference on Neural Information Processing Systems, NIPS 2006
CountryCanada
CityVancouver, BC
Period12/4/0612/7/06

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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    Wood, F., & Griffiths, T. L. (2007). Particle filtering for nonparametric Bayesian matrix factorization. In Advances in Neural Information Processing Systems 19 - Proceedings of the 2006 Conference (pp. 1513-1520). (Advances in Neural Information Processing Systems).