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Non-negative matrix factorization for parameter estimation in hidden Markov models

TitleNon-negative matrix factorization for parameter estimation in hidden Markov models
Publication TypeConference Paper
Year of Publication2010
AuthorsLakshminarayanan, B., and R. Raich
Conference Name2010 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
Pagination89 - 94
Date Published08/2010
PublisherIEEE
Conference LocationKittila, Finland
ISBN Number978-1-4244-7875-0
Abstract

Hidden Markov models are well-known in analysis of random processes, which exhibit temporal or spatial structure and have been successfully applied to a wide variety of applications such as but not limited to speech recognition, musical scores, handwriting, and bio-informatics. We present a novel algorithm for estimating the parameters of a hidden Markov model through the application of a non-negative matrix factorization to the joint probability distribution of two consecutive observations. We start with the discrete observation model and extend the results to the continuous observation model through a non-parametric approach of kernel density estimation. For both the cases, we present results on a toy example and compare the performance with the Baum-Welch algorithm.

DOI10.1109/MLSP.2010.5589231