NIPS Proceedingsβ

Spectral Learning of Mixture of Hidden Markov Models

Part of: Advances in Neural Information Processing Systems 27 (NIPS 2014)

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Authors

Conference Event Type: Poster

Abstract

In this paper, we propose a learning approach for the Mixture of Hidden Markov Models (MHMM) based on the Method of Moments (MoM). Computational advantages of MoM make MHMM learning amenable for large data sets. It is not possible to directly learn an MHMM with existing learning approaches, mainly due to a permutation ambiguity in the estimation process. We show that it is possible to resolve this ambiguity using the spectral properties of a global transition matrix even in the presence of estimation noise. We demonstrate the validity of our approach on synthetic and real data.