Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/8199
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Chatzis, Sotirios P. | - |
dc.contributor.author | Kosmopoulos, Dimitrios | - |
dc.contributor.other | Χατζής, Σωτήριος Π. | - |
dc.date.accessioned | 2016-01-18T10:39:38Z | - |
dc.date.available | 2016-01-18T10:39:38Z | - |
dc.date.issued | 2015-01-01 | - |
dc.identifier.citation | IEEE Transactions on Neural Networks and Learning Systems, 2015, vol. 26, no. 1, pp. 70-83 | en_US |
dc.identifier.issn | 2162237X | - |
dc.identifier.uri | https://hdl.handle.net/20.500.14279/8199 | - |
dc.description.abstract | In this paper, we propose a Gaussian process model for analysis of nonlinear time series. Formulation of our model is based on the consideration that the observed data are functions of latent variables, with the associated mapping between observations and latent representations modeled through Gaussian process priors. In addition, to capture the temporal dynamics in the modeled data, we assume that subsequent latent representations depend on each other on the basis of a hidden Markov prior imposed over them. Derivation of our model is performed by marginalizing out the model parameters in closed form by using Gaussian process priors for observation mappings, and appropriate stick-breaking priors for the latent variable (Markovian) dynamics. This way, we eventually obtain a nonparametric Bayesian model for dynamical systems that accounts for uncertainty in the modeled data.We provide efficient inference algorithms for our model on the basis of a truncated variational Bayesian approximation. We demonstrate the efficacy of our approach considering a number of applications dealing with real-world data, and compare it to related state-of-the-art approaches. | en_US |
dc.format | en_US | |
dc.language.iso | en | en_US |
dc.relation.ispartof | IEEE transactions on neural networks and learning systems | en_US |
dc.rights | © IEEE | en_US |
dc.subject | Gaussian process | en_US |
dc.subject | Stick-breaking process | en_US |
dc.subject | Markovian dynamics | en_US |
dc.subject | Latent manifold | en_US |
dc.title | A latent manifold markovian dynamics gaussian process | en_US |
dc.type | Article | en_US |
dc.collaboration | Cyprus University of Technology | en_US |
dc.collaboration | Hellenic Mediterranean University | en_US |
dc.subject.category | Electrical Engineering - Electronic Engineering - Information Engineering | en_US |
dc.review | Peer Reviewed | en |
dc.country | Greece | en_US |
dc.country | Cyprus | en_US |
dc.subject.field | Engineering and Technology | en_US |
dc.identifier.doi | 10.1109/TNNLS.2014.2311073 | en_US |
dc.dept.handle | 123456789/134 | en |
cut.common.academicyear | 2019-2020 | en_US |
item.fulltext | With Fulltext | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | open | - |
item.openairecristype | http://purl.org/coar/resource_type/c_6501 | - |
item.openairetype | article | - |
item.languageiso639-1 | en | - |
crisitem.journal.journalissn | 2162237X | - |
crisitem.journal.publisher | IEEE | - |
crisitem.author.dept | Department of Electrical Engineering, Computer Engineering and Informatics | - |
crisitem.author.faculty | Faculty of Engineering and Technology | - |
crisitem.author.orcid | 0000-0002-4956-4013 | - |
crisitem.author.parentorg | Faculty of Engineering and Technology | - |
Appears in Collections: | Άρθρα/Articles |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Chatzis.pdf | 369 kB | Adobe PDF | View/Open |
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