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Hidden Markov Model Framework Using Independent Component Analysis Mixture Model | IEEE Conference Publication | IEEE Xplore

Hidden Markov Model Framework Using Independent Component Analysis Mixture Model


Abstract:

This paper describes a novel method for the analysis of sequential data that exhibits strong non-Gaussianities. In particular, we extend the classical continuous hidden M...Show More

Abstract:

This paper describes a novel method for the analysis of sequential data that exhibits strong non-Gaussianities. In particular, we extend the classical continuous hidden Markov model (HMM) by modeling the observation densities as a mixture of non-Gaussian distributions. In order to obtain a parametric representation of the densities, we apply the independent component analysis (ICA) mixture model to the observations such that each non-Gaussian mixture component is associated with a standard ICA. Under this new framework, we develop the re-estimation formulas for the three fundamental HMM problems, namely, likelihood computation, state sequence estimation, and model parameter learning. The simulations also validate the theoretical results
Date of Conference: 14-19 May 2006
Date Added to IEEE Xplore: 24 July 2006
Print ISBN:1-4244-0469-X

ISSN Information:

Conference Location: Toulouse

References

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