Variational Infinite Hidden Conditional Random Fields
File(s)
Accepted version
Author(s)
Bousmalis, K
Zafeiriou, S
Morency, LP
Pantic, M
Ghahramani, Z
Type
Journal Article
Abstract
Hidden Conditional Random Fields (HCRFs) are discriminative latent variable models which have been shown to successfully learn the hidden structure of a given classification problem. An Infinite Hidden Conditional Random Field is a Hidden Conditional Random Field with a countably infinite number of hidden states, which rids us not only of the necessity to specify a priori a fixed number of hidden states available but also of the problem of overfitting. Markov chain Monte Carlo (MCMC) sampling algorithms are often employed for inference in such models. However, convergence of such algorithms is rather difficult to verify, and as the complexity of the task at hand increases the computational cost of such algorithms often becomes prohibitive. These limitations can be overcome by variational techniques. In this paper, we present a generalized framework for infinite HCRF models, and a novel variational inference approach on a model based on coupled Dirichlet Process Mixtures, the HCRF–DPM. We show that the variational HCRF–DPM is able to converge to a correct number of represented hidden states, and performs as well as the best parametric HCRFs —chosen via cross–validation— for the difficult tasks of recognizing instances of agreement, disagreement, and pain in audiovisual sequences.
Date Issued
2015-01-01
Date Acceptance
2015-01-01
Citation
IEEE transactions on Pattern Analysis and Machine Intelligence, 2015, PP (99)
ISSN
0162-8828
Publisher
IEEE
Journal / Book Title
IEEE transactions on Pattern Analysis and Machine Intelligence
Volume
PP
Issue
99
Copyright Statement
© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.