Accession Number:

ADA629956

Title:

A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation

Descriptive Note:

Corporate Author:

CALIFORNIA UNIV IRVINE SCHOOL OF INFORMATION AND COMPUTER SCIENCE

Personal Author(s):

Report Date:

2007-09-07

Pagination or Media Count:

9.0

Abstract:

Latent Dirichlet allocation LDA is a Bayesian network that has recently gained much popularity in applications ranging from document modeling to computer vision. Due to the large scale nature of these applications, current inference procedures like variational Bayes and Gibbs sampling have been found lacking. In this paper we propose the collapsed variational Bayesian inference algorithm for LDA, and show that it is computationally efficient, easy to implement and significantly more accurate than standard variational Bayesian inference for LDA.

Subject Categories:

  • Numerical Mathematics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE