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Conference Paper: Gaussian process dynamical models
Title | Gaussian process dynamical models |
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Authors | |
Issue Date | 2005 |
Citation | Advances in Neural Information Processing Systems, 2005, p. 1441-1448 How to Cite? |
Abstract | This 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 Identifier | http://hdl.handle.net/10722/192711 |
ISSN | 2020 SCImago Journal Rankings: 1.399 |
DC Field | Value | Language |
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dc.contributor.author | Wang, JM | en_US |
dc.contributor.author | Fleet, DJ | en_US |
dc.contributor.author | Hertzmann, A | en_US |
dc.date.accessioned | 2013-11-20T04:57:07Z | - |
dc.date.available | 2013-11-20T04:57:07Z | - |
dc.date.issued | 2005 | en_US |
dc.identifier.citation | Advances in Neural Information Processing Systems, 2005, p. 1441-1448 | en_US |
dc.identifier.issn | 1049-5258 | en_US |
dc.identifier.uri | http://hdl.handle.net/10722/192711 | - |
dc.description.abstract | This 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.language | eng | en_US |
dc.relation.ispartof | Advances in Neural Information Processing Systems | en_US |
dc.title | Gaussian process dynamical models | en_US |
dc.type | Conference_Paper | en_US |
dc.description.nature | link_to_subscribed_fulltext | en_US |
dc.identifier.scopus | eid_2-s2.0-84864069214 | en_US |
dc.identifier.spage | 1441 | en_US |
dc.identifier.epage | 1448 | en_US |
dc.identifier.issnl | 1049-5258 | - |