The Baum-Welch algorithm together with its derivatives and variations has been the main technique for learning hidden Markov models (HMMs) from observational data. We present an HMM learning algorithm based on the nonnegative matrix factorization (NMF) of higher order Markovian statistics that is structurally different from the Baum-Welch and its associated approaches. The described algorithm supports estimation of the number of recurrent states of an HMM and iterates the NMF algorithm to improve the learned HMM parameters. Numerical examples are provided as well. Index Terms-Hidden Markov models (HMMs), machine learning, nonnegative matrix factorization (NMF).
REF consider the identification of a hidden Markov model from the observed symbols sequence using nonnegative matrix factorization.
Learning Hidden Markov Models Using Nonnegative Matrix Factorization
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Mathematics Computer Science