Alpha-EM gives fast hidden Markov model estimation: Derivation and evaluation of alpha-HMM

Yasuo Matsuyama; Ryunosuke Hayashi

doi:10.1109/IJCNN.2010.5596959

Alpha-EM gives fast hidden Markov model estimation: Derivation and evaluation of alpha-HMM

Yasuo Matsuyama^*, Ryunosuke Hayashi

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Citations (Scopus)

Abstract

A fast learning algorithm for Hidden Markov Models is derived starting from convex divergence optimization. This method utilizes the alpha-logarithm as a surrogate function for the traditional logarithm to process the likelihood ratio. This enables the utilization of a stronger curvature than the logarithm. This paper's method includes the ordinary Baum-Welch re-estimation algorithm as a proper subset. The presented algorithm shows fast learning by utilizing time-shifted information during the progress of iterations. The computational complexity of this algorithm, which directly affects the CPU time, remains almost the same as the logarithmic one since only stored results are utilized for the speedup. Software implementation and speed are examined in the test data. The results showed that the presented method is creditable.

Original language	English
Title of host publication	Proceedings of the International Joint Conference on Neural Networks
DOIs	https://doi.org/10.1109/IJCNN.2010.5596959
Publication status	Published - 2010
Event	2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010 - Barcelona Duration: 2010 Jul 18 → 2010 Jul 23

Other

Other	2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
City	Barcelona
Period	10/7/18 → 10/7/23

ASJC Scopus subject areas

Software
Artificial Intelligence

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10.1109/IJCNN.2010.5596959

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Matsuyama, Y & Hayashi, R 2010, Alpha-EM gives fast hidden Markov model estimation: Derivation and evaluation of alpha-HMM. in Proceedings of the International Joint Conference on Neural Networks., 5596959, 2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010, Barcelona, 10/7/18. https://doi.org/10.1109/IJCNN.2010.5596959

@inproceedings{34a4c4272a914928a2891fcaffbda0ac,

title = "Alpha-EM gives fast hidden Markov model estimation: Derivation and evaluation of alpha-HMM",

abstract = "A fast learning algorithm for Hidden Markov Models is derived starting from convex divergence optimization. This method utilizes the alpha-logarithm as a surrogate function for the traditional logarithm to process the likelihood ratio. This enables the utilization of a stronger curvature than the logarithm. This paper's method includes the ordinary Baum-Welch re-estimation algorithm as a proper subset. The presented algorithm shows fast learning by utilizing time-shifted information during the progress of iterations. The computational complexity of this algorithm, which directly affects the CPU time, remains almost the same as the logarithmic one since only stored results are utilized for the speedup. Software implementation and speed are examined in the test data. The results showed that the presented method is creditable.",

author = "Yasuo Matsuyama and Ryunosuke Hayashi",

year = "2010",

doi = "10.1109/IJCNN.2010.5596959",

language = "English",

isbn = "9781424469178",

booktitle = "Proceedings of the International Joint Conference on Neural Networks",

note = "2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010 ; Conference date: 18-07-2010 Through 23-07-2010",

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T2 - 2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010

AU - Matsuyama, Yasuo

AU - Hayashi, Ryunosuke

PY - 2010

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N2 - A fast learning algorithm for Hidden Markov Models is derived starting from convex divergence optimization. This method utilizes the alpha-logarithm as a surrogate function for the traditional logarithm to process the likelihood ratio. This enables the utilization of a stronger curvature than the logarithm. This paper's method includes the ordinary Baum-Welch re-estimation algorithm as a proper subset. The presented algorithm shows fast learning by utilizing time-shifted information during the progress of iterations. The computational complexity of this algorithm, which directly affects the CPU time, remains almost the same as the logarithmic one since only stored results are utilized for the speedup. Software implementation and speed are examined in the test data. The results showed that the presented method is creditable.

AB - A fast learning algorithm for Hidden Markov Models is derived starting from convex divergence optimization. This method utilizes the alpha-logarithm as a surrogate function for the traditional logarithm to process the likelihood ratio. This enables the utilization of a stronger curvature than the logarithm. This paper's method includes the ordinary Baum-Welch re-estimation algorithm as a proper subset. The presented algorithm shows fast learning by utilizing time-shifted information during the progress of iterations. The computational complexity of this algorithm, which directly affects the CPU time, remains almost the same as the logarithmic one since only stored results are utilized for the speedup. Software implementation and speed are examined in the test data. The results showed that the presented method is creditable.

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BT - Proceedings of the International Joint Conference on Neural Networks

Y2 - 18 July 2010 through 23 July 2010

ER -

Alpha-EM gives fast hidden Markov model estimation: Derivation and evaluation of alpha-HMM

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