A recursive learning algorithm for model reduction of Hidden Markov Models

Kun Deng, Prashant G. Mehta, Sean P. Meyn, Mathukumalli Vidyasagar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011
Pages4674-4679
Number of pages6
DOIs
StatePublished - Dec 1 2011
Event2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011 - Orlando, FL, United States
Duration: Dec 12 2011Dec 15 2011

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0191-2216

Other

Other2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011
CountryUnited States
CityOrlando, FL
Period12/12/1112/15/11

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

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