We present a variational Bayesian inference algorithm for the stick-breaking construction of the beta process. We derive an alternate representation of the beta process that is amenable to variational inference, and present a bound relating the truncated beta process to its infinite counterpart. We assess performance on two matrix factorization problems, using a non-negative factorization model and a linear-Gaussian model. Copyright 2011 by the author(s)/owner(s).
|Original language||English (US)|
|Title of host publication||Proceedings of the 28th International Conference on Machine Learning, ICML 2011|
|Number of pages||8|
|State||Published - Oct 7 2011|