A spectral algorithm for inference in hidden semi-Markov models

Igor Melnyk, Arindam Banerjee

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

Abstract

Hidden semi-Markov models (HSMMs) are latent variable models which allow latent state persistence and can be viewed as a generalization of the popular hidden Markov models (HMMs). In this paper, we introduce a novel spectral algorithm to perform inference in HSMMs. Our approach is based on estimating certain sample moments, whose order depends only logarithmically on the maximum length of the hidden state persistence. Moreover, the algorithm requires only a few spectral decompositions and is therefore computationally efficient. Empirical evaluations on synthetic and real data demonstrate the promise of the algorithm.

Original languageEnglish (US)
Pages (from-to)690-698
Number of pages9
JournalJournal of Machine Learning Research
Volume38
StatePublished - Jan 1 2015
Event18th International Conference on Artificial Intelligence and Statistics, AISTATS 2015 - San Diego, United States
Duration: May 9 2015May 12 2015

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