Learning Complex Uncertain States Changes via Asymmetric Hidden Markov Models: an Industrial Case

Marcos L.P. Bueno, Arjen Hommersom, Peter J.F. Lucas, Sicco Verwer, Alexis Linard

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

Abstract

In many problems involving multivariate time series, Hidden Markov Models (HMMs) are often employed to model complex behavior over time. HMMs can, however, require large number of states, that can lead to overfitting issues especially when limited data is available. In this work, we propose a family of models called Asymmetric Hidden Markov Models (HMM-As), that generalize the emission distributions to arbitrary Bayesian-network distributions. The new model allowsfor state-specific graphical structures defined over the space of observable features, what renders more compact state spaces and hence a better handling of the complexity-overfitting trade-off. We first define asymmetric HMMs, followed by the definition of a learning procedure inspired on the structural expectation-maximization framework allowing for decomposing learning per state. Then, we relate representation aspects of HMM-As to standard and independent HMMs. The last contribution of the paper is a set of experiments that elucidate the behavior of asymmetric HMMs on practical scenarios, including simulations and industry-based scenarios. The empirical results indicate that HMMs are limited when learning structured distributions, what is prevented by the more parsimonious representation of HMM-As. Furthermore, HMM-As showed to be promising in uncovering multiple graphical structures and providing better model fit in a case study from the domain of large-scale printers, thus providing additional problem insight.
Original languageEnglish
Title of host publicationProceedings of the Eighth International Conference on Probabilistic Graphical Models
Subtitle of host publicationVolume 52 of the JMLR Workshop and Conference Proceedings: PGM 2016, Lugano, 6–9 September 2016
EditorsAlessandro Antonucci, Giorgio Corani, Cassio Polpo Campos
PublisherPMLR
Pages50-61
Number of pages12
Volume52
Publication statusPublished - 2016
EventInternational Conference on Probabilistic Graphical Models 2016 - Università della Svizzera Italiana (USI), Lugano, Switzerland
Duration: 6 Sep 20169 Sep 2016
Conference number: 8
https://www2.idsia.ch/cms/pgm/

Conference

ConferenceInternational Conference on Probabilistic Graphical Models 2016
Abbreviated titlePGM 2016
CountrySwitzerland
CityLugano
Period6/09/169/09/16
Internet address

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  • Cite this

    Bueno, M. L. P., Hommersom, A., Lucas, P. J. F., Verwer, S., & Linard, A. (2016). Learning Complex Uncertain States Changes via Asymmetric Hidden Markov Models: an Industrial Case. In A. Antonucci, G. Corani, & C. P. Campos (Eds.), Proceedings of the Eighth International Conference on Probabilistic Graphical Models: Volume 52 of the JMLR Workshop and Conference Proceedings: PGM 2016, Lugano, 6–9 September 2016 (Vol. 52, pp. 50-61). PMLR. http://proceedings.mlr.press/v52/bueno16.pdf