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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.ORCID iD: 0000-0002-1927-1690
2016 (English)In: Proceedings of the IEEE Conference on Decision and Control, IEEE conference proceedings, 2016, 4011-4016 p.Conference paper, Published paper (Refereed)
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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
Identifiers
URN: urn:nbn:se:kth:diva-188258DOI: 10.1109/CDC.2015.7402843ISI: 000381554504032Scopus ID: 2-s2.0-84962016914ISBN: 9781479978861 (print)OAI: oai:DiVA.org:kth-188258DiVA: diva2:937432
Conference
54th IEEE Conference on Decision and Control, CDC 2015, 15 December 2015 through 18 December 2015
Note

QC 20160615

Available from: 2016-06-15 Created: 2016-06-09 Last updated: 2016-12-20Bibliographically approved

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Publisher's full textScopushttp://cdc2015.ieeecss.org/

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CiteExportLink to record
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