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.
|Number of pages
|Journal of Machine Learning Research
|Published - Jan 1 2015
|18th International Conference on Artificial Intelligence and Statistics, AISTATS 2015 - San Diego, United States
Duration: May 9 2015 → May 12 2015