NDLI: A recursive learning algorithm for model reduction of Hidden Markov Models

Content Provider	IEEE Xplore Digital Library
Author	Deng, Kun Mehta, Prashant G. Meyn, Sean P. Vidyasagar, Mathukumalli
Copyright Year	2011
Description	Author affiliation: Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, 1308 West Main Street, 61801, USA (Deng, Kun; Mehta, Prashant G.; Meyn, Sean P.) \|\| Department of Bioengineering, University of Texas at Dallas, 800 W. Campbell Road, Richardson, 75080, USA (Vidyasagar, Mathukumalli)
Abstract	This paper is concerned with a recursive learning algorithm for model reduction of Hidden Markov Models (HMMs) with finite state space and finite observation space. The state space is aggregated/partitioned to reduce the complexity of the HMM. The optimal aggregation is obtained by minimizing the Kullback-Leibler divergence rate between the laws of the observation process. The optimal aggregated HMM is given as a function of the partition function of the state space. The optimal partition is obtained by using a recursive stochastic approximation learning algorithm, which can be implemented through a single sample path of the HMM. Convergence of the algorithm is established using ergodicity of the filtering process and standard stochastic approximation arguments.
Starting Page	4674
Ending Page	4679
File Size	366483
Page Count	6
File Format	PDF
ISBN	9781612848006
ISSN	07431546
e-ISBN	9781612848013
e-ISBN	9781612847993
DOI	10.1109/CDC.2011.6160826
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Maximum likelihood estimation Optimization Convergence Partitioning algorithms Markov processes Approximation algorithms
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Control and Systems Engineering Modeling and Simulation

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