Zero knowledge hidden Markov model inference

Authors:
J. M. Schwier;R. R. Brooks;C. Griffin;S. Bukkapatnam
Affiliations:
Holcombe Department of Electrical and Computer Engineering, Clemson University, P.O. Box 340915, Clemson, SC 29634, United States;Holcombe Department of Electrical and Computer Engineering, Clemson University, P.O. Box 340915, Clemson, SC 29634, United States;Communications, Navigation and Information Office, The Applied Research Laboratory, The Pennsylvania State University, University Park, PA 16804, United States;Oklahoma State University, School of Industrial Engineering and Management, EN 322, Stillwater, OK 74078, United States
Venue:
Pattern Recognition Letters
Year:
2009

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Abstract

Hidden Markov models (HMMs) are widely used in pattern recognition. HMM construction requires an initial model structure that is used as a starting point to estimate the model's parameters. To construct a HMM without a priori knowledge of the structure, we use an approach developed by Crutchfield and Shalizi that requires only a sequence of observations and a maximum data window size. Values of the maximum data window size that are too small result in incorrect models being constructed. Values that are too large reduce the number of data samples that can be considered and exponentially increase the algorithm's computational complexity. In this paper, we present a method for automatically inferring this parameter directly from training data as part of the model construction process. We present theoretical and experimental results that confirm the utility of the proposed extension.