Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/4146
Title: | A Reservoir-driven non-stationary hidden markov model |
Authors: | Demiris, Yiannis Chatzis, Sotirios P. |
metadata.dc.contributor.other: | Χατζής, Σωτήριος Π. Δεμίρης, Γιάννης |
Major Field of Science: | Engineering and Technology |
Field Category: | Electrical Engineering - Electronic Engineering - Information Engineering |
Keywords: | Pattern recognition;Computer science;Markov processes;Dirichlet process;Reservoir;Hidden Markov model |
Issue Date: | May-2012 |
Source: | Pattern recognition, 2012, vol. 45, no. 11, pp. 3985–3996 |
Volume: | 45 |
Issue: | 11 |
Start page: | 3985 |
End page: | 3996 |
Journal: | Pattern recognition |
Abstract: | In this work, we propose a novel approach towards sequential data modeling that leverages the strengths of hidden Markov models and echo-state networks (ESNs) in the context of non-parametric Bayesian inference approaches. We introduce a non-stationary hidden Markov model, the time-dependent state transition probabilities of which are driven by a high-dimensional signal that encodes the whole history of the modeled observations, namely the state vector of a postulated observations-driven ESN reservoir. We derive an efficient inference algorithm for our model under the variational Bayesian paradigm, and we examine the efficacy of our approach considering a number of sequential data modeling applications |
URI: | https://hdl.handle.net/20.500.14279/4146 |
ISSN: | 00313203 |
DOI: | 10.1016/j.patcog.2012.04.018 |
Rights: | © 2012 Elsevier. All rights reserved. Attribution-NonCommercial-NoDerivs 3.0 United States |
Type: | Article |
Affiliation : | Cyprus University of Technology Imperial College London |
Publication Type: | Peer Reviewed |
Appears in Collections: | Άρθρα/Articles |
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