Open AccessModified student's t ‐hidden Markov model for pattern recognition and classification
Author(s) -
Zhang Hui,
Wu Qing Ming Jonathan,
Nguyen Thanh Minh
Publication year - 2013
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
eISSN - 1751-9683
pISSN - 1751-9675
DOI - 10.1049/iet-spr.2012.0315
Subject(s) - hidden markov model , outlier , robustness (evolution) , mixture model , pattern recognition (psychology) , gaussian , student's t distribution , hidden semi markov model , maximum entropy markov model , artificial intelligence , markov model , forward algorithm , computer science , variable order markov model , markov chain , mathematics , algorithm , machine learning , econometrics , volatility (finance) , biochemistry , chemistry , physics , quantum mechanics , gene , autoregressive conditional heteroskedasticity
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.