Abstract
Hidden Markov Models(HMM) have proved to be a successful modeling paradigm for dynamic and spatial processes in many domains, such as speech recognition, genomics, and general sequence alignment. Typically, in these applications, the model structures are predefined by domain experts. Therefore, the HMM learning problem focuses on the learning of the parameter values of the model to fit the given data sequences. However, when one considers other domains, such as, economics and physiology, model structure capturing the system dynamic behavior is not available. In order to successfully apply the HMM methodology in these domains, it is important that a mechanism is available for automatically deriving the model structure from the data. This paper presents a HMM learning procedure that simultaneously learns the model structure and the maximum likelihood parameter values of a HMM from data. The HMM model structures are derived based on the Bayesian model selection methodology. In addition, we introduce a new initialization procedure for HMM parameter value estimation based on the K‐means clustering method. Experimental results with artificially generated data show the effectiveness of the approach.
Highlights
The Hidden Markov Model (HMM) methodology has been applied extensively in many domains for efficient modeling of multi-dimensional sequence data
Our focus in this paper is on modeling dynamic system behavior by learning the HMMs from temporal sequence data collected from the system
To take advantage of the characteristics of the Bayesian model selection criterion functions, a model expansion search control structure is employed that examines HMMs of gradually larger sizes, and stops the search process when further expansion of model size does not improve the quality of the model
Summary
The Hidden Markov Model (HMM) methodology has been applied extensively in many domains for efficient modeling of multi-dimensional sequence data. Examples of successful applications include the speech recognition domain for temporal sequence modeling [1], and the genomics domain for spatial sequence modeling [2]. The HMM methodology is a probabilistic state based approach, where the set of system states that govern the dynamic behavior or spatial characteristics may not be directly observable, the term “hidden”, the hidden states manifest indirectly as multidimensional output sequences that are directly observable. The HMM learning task starts with sequence data, and attempts to fit a probabilistic state transition model that best describes the data. HMM methodology can be applied to modeling tasks for any real world system that is dynamic in nature, for example, systems in economics, social science, and medical domains.
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