Asymmetric hidden Markov models
| Autor: | Arjen Hommersom, Alexis Linard, Peter J. F. Lucas, Marcos L. P. Bueno |
|---|---|
| Přispěvatelé: | Department Computer Science, RS-Research Line Resilience (part of LIRS program) |
| Rok vydání: | 2017 |
| Předmět: |
Time series
Theoretical computer science 02 engineering and technology Overfitting Model selection Markov model Machine learning computer.software_genre Unsupervised learning 01 natural sciences Theoretical Computer Science Structure learning 010104 statistics & probability Artificial Intelligence Software Science 0202 electrical engineering electronic engineering information engineering Hidden Markov models 0101 mathematics Representation (mathematics) Hidden Markov model Mathematics business.industry Applied Mathematics Bayesian network Bayesian networks ComputingMethodologies_PATTERNRECOGNITION INFERENCE 020201 artificial intelligence & image processing Artificial intelligence Hidden semi-Markov model business computer Software |
| Zdroj: | International Journal of Approximate Reasoning, 88, 169-191 Bueno, M L P, Hommersom, A, Lucas, P J F & Linard, A 2017, ' Asymmetric hidden Markov models ', International Journal of Approximate Reasoning, vol. 88, pp. 169-191 . https://doi.org/10.1016/j.ijar.2017.05.011 International Journal of Approximate Reasoning, 88, 169-191. Elsevier Science Inc. International Journal of Approximate Reasoning, 88, pp. 169-191 |
| ISSN: | 0888-613X |
| DOI: | 10.1016/j.ijar.2017.05.011 |
| Popis: | In many problems involving multivariate time series, hidden Markov models (HMMs) are often employed for modeling complex behavior over time. HMMs can, however, require large number of states, what can lead to poor problem insight and model overfitting, especially when limited data is available. In this paper, we further investigate the family of asymmetric hidden Markov models (HMM-As), which generalize the emission distributions to arbitrary Bayesian-network distributions, allowing for state-specific graphical structures in the feature space. As a consequence, HMM-As are able to render more compact state spaces, thus from a learning perspective HMM-As can better handle the complexity-overfitting trade-off. In this paper, we study representation properties of asymmetric and symmetric HMMs, as well as provide a learning algorithm for HMM-As. We provide empirical results based on simulations for comparing HMM-As with symmetric and other asymmetry-aware models, showing that modeling more general asymmetries can be very effective. We also consider real-world datasets from several domains, aiming to show that multiple graphical structures underlying data can be identified and are able to provide additional problem insight. Although learning HMM-As can be more complex, it is shown that it is feasible in practice due to their ability to maintain compact state spaces, yet more expressive ones. |
| Databáze: | OpenAIRE |
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