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Recursive identification of chain dynamics in Hidden Markov Models using Non-Negative Matrix Factorization
KTH, School of Electrical Engineering (EES), Automatic Control.
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
2016 (English)In: Proceedings of the IEEE Conference on Decision and Control, IEEE conference proceedings, 2016, 4011-4016 p.Conference paper (Refereed)Text
Abstract [en]

Hidden Markov Models (HMMs) and associated Markov modulated time series are widely used for estimation and control in e.g. robotics, econometrics and bioinformatics. In this paper, we modify and extend a recently proposed approach in the machine learning literature that uses the method of moments and a Non-Negative Matrix Factorization (NNMF) to estimate the parameters of an HMM. In general, the method aims to solve a constrained non-convex optimization problem. In this paper, it is shown that if the observation probabilities of the HMM are known, then estimating the transition probabilities reduces to a convex optimization problem. Three recursive algorithms are proposed for estimating the transition probabilities of the underlying Markov chain, one of which employs a generalization of the Pythagorean trigonometric identity to recast the problem into a non-constrained optimization problem. Numerical examples are presented to illustrate how these algorithms can track slowly time-varying transition probabilities.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2016. 4011-4016 p.
National Category
Control Engineering
URN: urn:nbn:se:kth:diva-188258DOI: 10.1109/CDC.2015.7402843ScopusID: 2-s2.0-84962016914ISBN: 9781479978861OAI: diva2:937432
54th IEEE Conference on Decision and Control, CDC 2015, 15 December 2015 through 18 December 2015

QC 20160615

Available from: 2016-06-15 Created: 2016-06-09 Last updated: 2016-06-15Bibliographically approved

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Mattila, RobertWahlberg, Bo
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