Hierarchical Learning of Hidden Markov Models with Clustering Regularization

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

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Detail(s)

Original languageEnglish
Title of host publicationProceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence (UAI 2021)
EditorsCassio de Campos, Marloes H. Maathuis
PublisherML Research Press
Pages1628-1638
Publication statusPublished - Jul 2021

Publication series

NameProceedings of Machine Learning Research
Volume161
ISSN (Print)2640-3498

Conference

Title37th Conference on Uncertainty in Artificial Intelligence, UAI 2021
CityVirtual, Online
Period27 - 30 July 2021

Abstract

Hierarchical learning of generative models is useful for representing and interpreting complex data. For instance, one application is to learn an HMM to represent an individual's eye fixations on a stimuli, and then cluster individuals' HMMs to discover common eye gaze strategies. However, learning the individual representation models from observations and clustering individual models to group models are often considered as two separate tasks.In this paper, we propose a novel tree structure variational Bayesian method to learn the individual model and group model simultaneously by treating the group models as the parents of individual models, so that the individual model is learned from observations and regularized by its parents, and conversely, the parent model will be optimized to best represent its children. Due to the regularization process, our method has advantages when the number of training samples decreases. Experimental results on the synthetic datasets demonstrate the effectiveness of the proposed method.

Citation Format(s)

Hierarchical Learning of Hidden Markov Models with Clustering Regularization. / Lan, Hui; Chan, Antoni B.
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence (UAI 2021). ed. / Cassio de Campos; Marloes H. Maathuis. ML Research Press, 2021. p. 1628-1638 (Proceedings of Machine Learning Research; Vol. 161).

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review