A Nonstationary Hidden Markov Model with Approximately Infinitely-Long Time-Dependencies - Chatzis, Sotirios P.; Kosmopoulos, Dimitrios I.; Papadourakis, George M. (My University, 2014)
Hidden Markov models (HMMs) are a popular approach for modeling sequential data, typically based on the assumption of a first-order Markov chain. In other words, only one-step back dependencies are modeled which is a rather unrealistic assumption in most applications. In this paper, we propose a method for postulating HMMs with approximately infinitely-long time-dependencies. Our approach considers the whole history of model states in the postulated dependencies, by making use of a recently proposed nonparametric Bayesian method for modeling label sequences with infinitely-long time dependencies, namely the sequence memoizer. We manage to derive training and inference algorithms for our model with computational costs identical to simple first-order HMMs, despite its entailed infinitely-long time-dependencies, by employing a mean-field-like approximation. The efficacy of our proposed model is experimentally demonstrated.
Keywords: Pattern Recognition; Computer Graphics; Image processing and computer vision; User interfaces and human computer interaction; Information systems applications (incl. Internet); Computational biology/bioinformatics