Structured Bayesian Gaussian process latent variable model
We introduce a Bayesian Gaussian process latent variable model that explicitly captures spatial correlations in data using a parameterized spatial kernel and leveraging structure-exploiting algebra on the model covariance matrices for computational tractability. Inference is made tractable through a collapsed variational bound with similar computational complexity to that of the traditional Bayesian GP-LVM. Inference over partially-observed test cases is achieved by optimizing a "partially-collapsed" bound. Modeling high-dimensional time series systems is enabled through use of a dynamical GP latent variable prior. Examples imputing missing data on images and super-resolution imputation of missing video frames demonstrate the model.
Code (0)
등록된 구현이 없습니다.
Tasks
ImputationmodelSuper-ResolutionTime SeriesTime Series AnalysisMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Structured Bayesian Gaussian process latent variable model: applications to data-driven dimensionality reduction and high-dimensional inversion
We introduce a methodology for nonlinear inverse problems using a variational Bayesian approach where the unknown quantity is a spatial field. A structured Bayesian Gaussian process latent variable model is used both to …
Dimensionality ReductionGaussian Process Latent Variable Flows for Massively Missing Data
Gaussian process latent variable models (GPLVM) are used to perform nonlinear and probabilistic dimensionality reduction. They extend Gaussian processes (GP) to the domain of unsupervised learning. The Bayesian incarnati…
Dimensionality ReductionGaussian ProcessesNormalising FlowsVariational InferenceBeta Process Non-negative Matrix Factorization with Stochastic Structured Mean-Field Variational Inference
Beta process is the standard nonparametric Bayesian prior for latent factor model. In this paper, we derive a structured mean-field variational inference algorithm for a beta process non-negative matrix factorization (NM…
Variational InferenceMonte Carlo Structured SVI for Two-Level Non-Conjugate Models
The stochastic variational inference (SVI) paradigm, which combines variational inference, natural gradients, and stochastic updates, was recently proposed for large-scale data analysis in conjugate Bayesian models and d…
Gaussian ProcessesTopic ModelsVariational InferenceVocal Bursts Valence PredictionMulti-view Bayesian optimisation in reduced dimension for engineering design
Bayesian optimisation is an adaptive sampling strategy for constructing a Gaussian process surrogate to emulate a black-box computational model with the aim of efficiently searching for the global minimum. However, Gauss…
Bayesian OptimisationGaussian ProcessesMULTI-VIEW LEARNING