Algorithms of the LDA model [REPORT]
Jaka \v{S}peh, Andrej Muhi\v{c}, Jan Rupnik
TL;DR
This paper reviews three algorithms for Latent Dirichlet Allocation, comparing their efficiency and effectiveness, and finds online variational Bayesian inference to be the fastest with good results.
Contribution
It provides a comparative analysis of variational and MCMC algorithms for LDA, highlighting the efficiency of online variational inference.
Findings
Online variational Bayesian inference is the fastest algorithm.
Online variational inference produces reasonably good results.
Comparison of time complexity and performance of three LDA algorithms.
Abstract
We review three algorithms for Latent Dirichlet Allocation (LDA). Two of them are variational inference algorithms: Variational Bayesian inference and Online Variational Bayesian inference and one is Markov Chain Monte Carlo (MCMC) algorithm -- Collapsed Gibbs sampling. We compare their time complexity and performance. We find that online variational Bayesian inference is the fastest algorithm and still returns reasonably good results.
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Taxonomy
TopicsBayesian Methods and Mixture Models · Gaussian Processes and Bayesian Inference · Statistical Methods and Inference