Collapsed Variational Dirichlet Process Mixture Models
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 2796-2800.
Abstract
Nonparametric Bayesian mixture models, in particular Dirichlet process (DP) mixture models, have shown great promise for density estimation and data clustering. Given the size of today's datasets, computational efficiency becomes an essential ingredient in the applicability of these techniques to real world data. We study and experimentally compare a number of variational Bayesian (VB) approximations to the DP mixture model. In particular we consider the standard VB approximation where parameters are assumed to be independent from cluster assignment variables, and a novel collapsed VB approximation where mixture weights are marginalized out. For both VB approximations we consider two different ways to approximate the DP, by truncating the stick-breaking construction, and by using a finite mixture model with a symmetric Dirichlet prior.
@inproceedings{ijcai2007p449,
title = {Collapsed Variational Dirichlet Process Mixture Models},
author = {Kurihara, Kenichi and Welling, Max and Teh, Yee Whye},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {2796--2800},
year = {2007},
url = {https://ijcai.org/proceedings/2007/449},
}
TY - CPAPER TI - Collapsed Variational Dirichlet Process Mixture Models AU - Kurihara, Kenichi AU - Welling, Max AU - Teh, Yee Whye T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 2796 EP - 2800 UR - https://ijcai.org/proceedings/2007/449 ER -Copied
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