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dc.contributor.authorDamianou, ACen
dc.contributor.authorTitsias, MKen
dc.contributor.authorLawrence, Neilen
dc.date.accessioned2020-01-21T14:28:09Z
dc.date.available2020-01-21T14:28:09Z
dc.date.issued2016-04-01en
dc.identifier.issn1532-4435
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/301116
dc.description.abstractThe Gaussian process latent variable model (GP-LVM) provides a flexible approach for non-linear dimensionality reduction that has been widely applied. However, the current approach for training GP-LVMs is based on maximum likelihood, where the latent projection variables are maximised over rather than integrated out. In this paper we present a Bayesian method for training GP-LVMs by introducing a non-standard variational inference framework that allows to approximately integrate out the latent variables and subsequently train a GP-LVM by maximising an analytic lower bound on the exact marginal likelihood. We apply this method for learning a GP-LVM from i.i.d. observations and for learning non-linear dynamical systems where the observations are temporally correlated. We show that a benefit of the variational Bayesian procedure is its robustness to overfitting and its ability to automatically select the dimensionality of the non-linear latent space. The resulting framework is generic, flexible and easy to extend for other purposes, such as Gaussian process regression with uncertain or partially missing inputs. We demonstrate our method on synthetic data and standard machine learning benchmarks, as well as challenging real world datasets, including high resolution video data.
dc.description.sponsorshipThis research was partially funded by the European research project EU FP7-ICT (Project Ref 612139 \WYSIWYD"), the Greek State Scholarships Foundation (IKY) and the University of She eld Moody endowment fund. We also thank Colin Litster and \Fit Fur Life" for allowing us to use their video les as datasets.
dc.titleVariational inference for latent variables and uncertain inputs in Gaussian processesen
dc.typeArticle
prism.publicationDate2016en
prism.publicationNameJournal of Machine Learning Researchen
prism.volume17en
dc.identifier.doi10.17863/CAM.48192
dcterms.dateAccepted2016-03-01en
rioxxterms.versionVoR*
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2016-04-01en
dc.contributor.orcidLawrence, Neil [0000-0001-9258-1030]
dc.identifier.eissn1533-7928
rioxxterms.typeJournal Article/Reviewen


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