@inproceedings{f657870db1e54033a92da87f3193a920,

title = "Particle Filtering for Nonparametric Bayesian Matrix Factorization",

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.",

author = "Frank Wood and Griffiths, {Thomas L.}",

note = "Funding Information: Acknowledgements This work was supported by both NIH-NINDS R01 NS 50967-01 as part of the NSF/NIH Collaborative Research in Computational Neuroscience Program and NSF grant 0631518. Publisher Copyright: {\textcopyright} NIPS 2006.All rights reserved; 19th International Conference on Neural Information Processing Systems, NIPS 2006 ; Conference date: 04-12-2006 Through 07-12-2006",

year = "2006",

language = "English (US)",

series = "NIPS 2006: Proceedings of the 19th International Conference on Neural Information Processing Systems",

publisher = "MIT Press Journals",

pages = "1513--1520",

editor = "Bernhard Scholkopf and Platt, {John C.} and Thomas Hofmann",

booktitle = "NIPS 2006",

address = "United States",

}