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Conference Paper: Gaussian process dynamical models

TitleGaussian process dynamical models
Authors
Issue Date2005
Citation
Advances in Neural Information Processing Systems, 2005, p. 1441-1448 How to Cite?
AbstractThis paper introduces Gaussian Process Dynamical Models (GPDM) for nonlinear time series analysis. A GPDM comprises a low-dimensional latent space with associated dynamics, and a map from the latent space to an observation space. We marginalize out the model parameters in closed-form, using Gaussian Process (GP) priors for both the dynamics and the observation mappings. This results in a nonparametric model for dynamical systems that accounts for uncertainty in the model. We demonstrate the approach on human motion capture data in which each pose is 62-dimensional. Despite the use of small data sets, the GPDM learns an effective representation of the nonlinear dynamics in these spaces.
Persistent Identifierhttp://hdl.handle.net/10722/192711
ISSN

 

DC FieldValueLanguage
dc.contributor.authorWang, JMen_US
dc.contributor.authorFleet, DJen_US
dc.contributor.authorHertzmann, Aen_US
dc.date.accessioned2013-11-20T04:57:07Z-
dc.date.available2013-11-20T04:57:07Z-
dc.date.issued2005en_US
dc.identifier.citationAdvances in Neural Information Processing Systems, 2005, p. 1441-1448en_US
dc.identifier.issn1049-5258en_US
dc.identifier.urihttp://hdl.handle.net/10722/192711-
dc.description.abstractThis paper introduces Gaussian Process Dynamical Models (GPDM) for nonlinear time series analysis. A GPDM comprises a low-dimensional latent space with associated dynamics, and a map from the latent space to an observation space. We marginalize out the model parameters in closed-form, using Gaussian Process (GP) priors for both the dynamics and the observation mappings. This results in a nonparametric model for dynamical systems that accounts for uncertainty in the model. We demonstrate the approach on human motion capture data in which each pose is 62-dimensional. Despite the use of small data sets, the GPDM learns an effective representation of the nonlinear dynamics in these spaces.en_US
dc.languageengen_US
dc.relation.ispartofAdvances in Neural Information Processing Systemsen_US
dc.titleGaussian process dynamical modelsen_US
dc.typeConference_Paperen_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.scopuseid_2-s2.0-84864069214en_US
dc.identifier.spage1441en_US
dc.identifier.epage1448en_US

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