Two-step Nonnegative Matrix Factorization Algorithm for the Approximate Realization of Hidden Markov Models

Details Two-step Nonnegative Matrix Factorization Algorithm for the Approximate Realization of Hidden Markov Models Two-step Nonnegative Matrix Factorization Algorithm for the Approximate Realization of Hidden Markov Models

2010-07-20

arxiv.org/abs/1007.3435v1

Mathematics

Optimization and Control

presented at MTNS2010 - Budapest, July 2010

Scientific paper

We propose a two-step algorithm for the construction of a Hidden Markov Model (HMM) of assigned size, i.e. cardinality of the state space of the underlying Markov chain, whose $n$-dimensional distribution is closest in divergence to a given distribution. The algorithm is based on the factorization of a pseudo Hankel matrix, defined in terms of the given distribution, into the product of a tall and a wide nonnegative matrix. The implementation is based on the nonnegative matrix factorization (NMF) algorithm. To evaluate the performance of our algorithm we produced some numerical simulations in the context of HMM order reduction.

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