A general technique for the construction of hidden Markov models (HMMs) from multiple-variable time-series observations in noisy experimental environments is set out. The proposed methodology provides an ICA-based feature-selection technique for determining the number, and the transition sequence, of underlying hidden states, along with the statistics of the observed-state emission characteristics. In retaining correlation information between features, the method is potentially far more general than Gaussian mixture model HMM parameterisation methods such as Baum-Welch re-estimation, to which we demonstrate our method reduces when an arbitrary separation of features, or an experimentally-limited feature-space is imposed. © Springer-Verlag 2004.
CITATION STYLE
Windridge, D., Bowden, R., & Kittler, J. (2004). A general strategy for hidden markov chain parameterisation in composite feature-spaces. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3138, 1069–1077. https://doi.org/10.1007/978-3-540-27868-9_118
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