@inproceedings{946e9e5a024145e88958585a1c4558c4,
title = "A recursive learning algorithm for model reduction of Hidden Markov Models",
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.",
author = "Kun Deng and Mehta, {Prashant G.} and Meyn, {Sean P.} and Mathukumalli Vidyasagar",
year = "2011",
doi = "10.1109/CDC.2011.6160826",
language = "English (US)",
isbn = "9781612848006",
series = "Proceedings of the IEEE Conference on Decision and Control",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4674--4679",
booktitle = "2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011",
address = "United States",
note = "2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011 ; Conference date: 12-12-2011 Through 15-12-2011",
}