Modified student's t-hidden Markov model for pattern recognition and classification

The Gaussian hidden Markov model has been successfully used in pattern recognition and classification applications; however, recently the Student's t-mixture model is regarded as an alternative to Gaussian mixture models, as it is more robust for outliers. The model using Student's t-mixtu...

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Bibliographic Details
Published in:IET signal processing 2013-05, Vol.7 (3), p.219-227
Main Authors: Zhang, Hui, Wu, Qing Ming Jonathan, Nguyen, Thanh Minh
Format: Article
Language:English
Subjects:
Applied sciences
Classification
component densities
Density
emission densities
Exact sciences and technology
expectation‐maximisation algorithm
Gaussian
Gaussian mixture models
Gaussian processes
gradient method
gradient methods
hidden Markov models
Information, signal and communications theory
latent components
Markov models
Mathematical models
modified student t‐hidden Markov model
optimal weight parameters
pattern classification
pattern classification applications
Pattern recognition
pattern recognition applications
real data
Robustness
scalar exponent regularization
Scalars
Signal processing
standard SHMM
synthetic data
Telecommunications and information theory
t‐mixture distribution
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Summary:The Gaussian hidden Markov model has been successfully used in pattern recognition and classification applications; however, recently the Student's t-mixture model is regarded as an alternative to Gaussian mixture models, as it is more robust for outliers. The model using Student's t-mixture distribution as its hidden state is the Student's t-hidden Markov model (SHMM). The authors propose a novel Student's t-hidden Markov model, which considers the relationship among Markov states, latent components and observations by introducing a regularising scalar exponent in the component densities of the model's emission densities. Moreover, the standard SHMM can be considered as a special case of the modified SHMM with the selection of proper parameter values. Finally, the authors adopt the gradient method to estimate optimal weight parameters. Simultaneously, the expectation–maximisation algorithm is used to fit the modified SHMM. Thus, our model is simple and easy to implement. The experimental results using synthetic and real data demonstrate the improved robustness of the proposed approach.
ISSN:1751-9675
1751-9683
1751-9683
DOI:10.1049/iet-spr.2012.0315