Hidden Markov model identifiability via tensors
Date
2013
Authors
Tune, P.
Nguyen, H.
Roughan, M.
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Conference paper
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IEEE International Symposium on Information Theory, ISIT 2013, 2013/ pp.2299-2303
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Paul Tune, Hung X. Nguyen and Matthew Roughan
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IEEE International Symposium on Information Theory (2013 : Istanbul)
Abstract
The prevalence of hidden Markov models (HMMs) in various applications of statistical signal processing and communications is a testament to the power and flexibility of the model. In this paper, we link the identifiability problem with tensor decomposition, in particular, the Canonical Polyadic decomposition. Using recent results in deriving uniqueness conditions for tensor decomposition, we are able to provide a necessary and sufficient condition for the identification of the parameters of discrete time finite alphabet HMMs. This result resolves a long standing open problem regarding the derivation of a necessary and sufficient condition for uniquely identifying an HMM. We then further extend recent preliminary work on the identification of HMMs with multiple observers by deriving necessary and sufficient conditions for identifiability in this setting.
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©2013 IEEE