Learning Hidden Markov Models Using Nonnegative Matrix Factorization

被引：15
|
作者：
Cybenko, George [1 ]
Crespi, Valentino [2 ]
机构：
[1] Dartmouth Coll, Thayer Sch Engn, Hanover, NH 03755 USA
[2] Calif State Univ Los Angeles, Dept Comp Sci, Los Angeles, CA 90032 USA
来源：
IEEE TRANSACTIONS ON INFORMATION THEORY | 2011年 / 57卷 / 06期
基金：
美国国家科学基金会;
关键词：
Hidden Markov models (HMMs); machine learning; nonnegative matrix factorization (NMF); FINITE-STATE MACHINES;
D O I：
10.1109/TIT.2011.2132490
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The Baum-Welch algorithm together with its derivatives and variations has been the main technique for learning hidden Markov models (HMMs) from observational data. We present an HMM learning algorithm based on the nonnegative matrix factorization (NMF) of higher order Markovian statistics that is structurally different from the Baum-Welch and its associated approaches. The described algorithm supports estimation of the number of recurrent states of an HMM and iterates the NMF algorithm to improve the learned HMM parameters. Numerical examples are provided as well.
引用
收藏
页码：3963 / 3970
页数：8
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