Please use this identifier to cite or link to this item:
|Title:||A Nonstationary Hidden Markov Model with Approximately Infinitely-Long Time-Dependencies||Authors:||Chatzis, Sotirios P.
Papadourakis, George M.
Image processing and computer vision
User interfaces and human computer interaction
Information systems applications (incl. Internet)
|Issue Date:||Dec-2014||Publisher:||Springer International Publishing||Source:||Advances in Visual Computing, 10th International Symposium, ISVC, Proceedings, Part I, 2014, Las Vegas, pages 51-62||Abstract:||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.||URI:||http://ktisis.cut.ac.cy/jspui/handle/10488/8582||ISBN:||978-3-319-14363-7
|Appears in Collections:||Δημοσιεύσεις σε συνέδρια/Conference papers|
Show full item record
Page view(s) 502
checked on Jan 23, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.