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Semisupervised Learning of Hidden Markov Models via a Homotopy Method
February 2009 (vol. 31 no. 2)
pp. 275-287
Shihao Ji, Duke University, Durham
Layne T. Watson, Virginia Polytechnic Institute and State Univeristy, Blacksburg
Lawrence Carin, Duke University, Durham
Hidden Markov model (HMM) classifier design is considered for the analysis of sequential data, incorporating both labeled and unlabeled data for training; the balance between the use of labeled and unlabeled data is controlled by an allocation parameter \lambda \in [0, 1), where \lambda = 0 corresponds to purely supervised HMM learning (based only on the labeled data) and \lambda = 1 corresponds to unsupervised HMM-based clustering (based only on the unlabeled data). The associated estimation problem can typically be reduced to solving a set of fixed-point equations in the form of a “natural-parameter homotopy.

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Index Terms:
Semisupervised learning, homotopy method, hidden Markov models (HMMs), supervised learning.
Citation:
Shihao Ji, Layne T. Watson, Lawrence Carin, "Semisupervised Learning of Hidden Markov Models via a Homotopy Method," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 2, pp. 275-287, Feb. 2009, doi:10.1109/TPAMI.2008.71
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