Low-rank spectral estimation algorithm of learning Markov model

Abstract

This paper proposes a low-rank spectral estimation algorithm of learning Markov model. First, an approximate projection algorithm for the rank-constrained frequency matrix set is proposed, and thereafter its local Lipschitzian error bound established. Then, we propose a low-rank spectral estimation algorithm for estimating the state transition frequency matrix and the probability matrix of Markov model by applying the approximate projection algorithm to correct the maximum likelihood estimation of the frequency matrix, and prove that there is only a multiplying constant difference in estimation errors between the low-rank spectral estimation and the maximum likelihood estimation under appropriate conditions. Finally, numerical comparisons with the prevailing maximum likelihood estimation, spectral estimation, and rank-constrained maximum likelihood estimation show that the low-rank spectral estimation algorithm is effective.

Graphical abstract

Keywords

Markov model / low-rank spectral estimation / error bound / approximate projection

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