On the Realization of Hidden Markov Models and Tensor Decomposition
Abstract
The minimum realization problem of hidden Markov models (HMM's) is a fundamental question of stationary discrete-time processes with a finite alphabet. It was shown in the literature that tensor decomposition methods give the hidden Markov model with the minimum number of states generically. However, the tensor decomposition approach does not solve the minimum HMM realization problem when the observation is a deterministic function of the state, which is an important class of HMM's not captured by a generic argument. In this paper, we show that the reduction of the number of rank-one tensors necessary to decompose the third-order tensor constructed from the probabilities of the process is possible when the reachable subspace is not the whole space or the null space is not the zero space. In fact, the rank of the tensor is not greater than the dimension of the effective subspace or the rank of the generalized Hankel matrix.
Cite
@article{arxiv.2008.11487,
title = {On the Realization of Hidden Markov Models and Tensor Decomposition},
author = {Yoshito Ohta},
journal= {arXiv preprint arXiv:2008.11487},
year = {2020}
}
Comments
This paper is accepted for the presentation at the 24th International Symposium on Mathematical Theory of Networks and Systems (MTNS 2020): Cambridge, UK