Bayesian Gaussian Process Latent Variable Model

Keywords

Overfitting, Latent variable, Gaussian process, Dimensionality reduction, Marginal likelihood

References

• Variational Learning of Inducing Variables in Sparse Gaussian ProcessesMichalis K. Titsias — 2009
• Gaussian processes:iterative sparse approximationsLehel Csató — 2002
• Learning for Larger Datasets with the Gaussian Process Latent Variable ModelNeil D. Lawrence — 2007
• Fast Forward Selection to Speed Up Sparse Gaussian Process RegressionMatthias Seeger, Christopher K. I. Williams, Neil D. Lawrence — 2003
• A Unifying View of Sparse Approximate Gaussian Process RegressionJoaquin Quiñonero-Candela, Carl Edward Rasmussen — 2005
• Analysis of multiphase flows using dual-energy gamma densitometry and neural networksChris Bishop, Gnadt James — 1993
• Hierarchical Gaussian process latent variable modelsNeil D. Lawrence, Andrew Moore — 2007
• Variational principal componentsChris Bishop — 1999
• Sparse Gaussian Processes using Pseudo-inputsEdward Snelson, Zoubin Ghahramani — 2005
• Gaussian Process Priors with Uncertain Inputs - Application to Multiple-Step Ahead Time Series ForecastingAgathe Girard, Carl Edward Rasmussen, Joaquin Quiñonero Candela — 2002
• Global Coordination of Local Linear ModelsSam T. Roweis, Lawrence K. Saul, Geoffrey E. Hinton — 2001
• Rank priors for continuous non-linear dimensionality reductionAndreas Geiger, Raquel Urtasun, Trevor Darrell — 2009
• Efficient Sampling for Gaussian Process Inference using Control VariablesNeil D. Lawrence, Magnus Rattray, Michalis K. Titsias — 2008
• Sparse On-Line Gaussian ProcessesLehel Csató, Manfred Opper — 2002
• Automatic Choice of Dimensionality for PCAThomas P. Minka — 2000
• Variational Inference for Bayesian Mixtures of Factor AnalysersZoubin Ghahramani, Matthew J. Beal — 1999
• Gaussian Process Dynamical ModelsJianguo Wang, Aaron Hertzmann, David M. Blei — 2005
• Bayesian PCAChris Bishop — 1998
• Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable ModelsLawrenceNeil — 2005
• Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable ModelsNeil D. Lawrence — 2005

Cited by

• Factor Models to Describe Linear and Non-linear Structure in High Dimensional Gene Expression DataV. Mayrink — 2011
• Nested Variational Compression in Deep Gaussian ProcessesJames Hensman, Neil D. Lawrence — 2014
• Exploratory studies for Gaussian Process Structural Equation ModelsYD Chiu — 2014
• On Sparse Variational Methods and the Kullback-Leibler Divergence between Stochastic ProcessesAlexander Matthews, James Hensman, Richard E. Turner — 2015
• Visual speech synthesis by learning joint probabilistic models of audio and videoSalil Deena — 2012
• Digital Signal ProcessingKaluri V. Rangarao, Ranjan K. Mallik — 2005
• Visualization and interpretability in probabilistic dimensionality reduction modelsAlessandra Tosi — 2014
• Model-based understanding of facial expressionsPatrick Sauer — 2013
• Automatic model construction with Gaussian processesDavid Duvenaud — 2014
• Gaussian Process for Noisy Inputs with Ordering ConstraintsCuong Dinh Tran, Vladimir Pavlović, Robert E. Kopp — 2015
• Model learning for robot control: a surveyDuy Nguyen-Tuong, Jan Peters — 2011
• Deep Gaussian processes and variational propagation of uncertaintyAndreas Damianou — 2015
• Topics in Modern Bayesian ComputationShaan Qamar — 2015
• A New Monte Carlo Based Algorithm for the Gaussian Process Classification ProblemAmir F. Atiya, Hatem A. Fayed, Ahmed H. Abdel-Gawad — 2013
• Dimensionality reduction for survival data via the Gaussian process latent variable modelJames E. Barrett, A C C Coolen — 2014
• High-Dimensional Probability Estimation with Deep Density ModelsOren Rippel, Ryan P. Adams — 2013
• Variational Inference for Sparse Spectrum Approximation in Gaussian Process RegressionYarin Gal, Richard E. Turner — 2015
• Approximate Riemannian Conjugate Gradient Learning for Fixed-Form Variational BayesAntti Honkela, Tapani Raiko, Mikael Kuusela — 2010
• Dimensionality Detection and Integration of Multiple Data Sources via the GP-LVMJames E. Barrett, A C C Coolen — 2013
• Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models - a Gentle TutorialYarin Gal, Mark van der Wilk — 2014

Related papers

• Gaussian Process Latent Variable Models for Visualisation of High Dimensional DataNeil D. Lawrence — 2003
• Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable ModelsNeil D. Lawrence — 2005
• Sparse Gaussian Processes using Pseudo-inputsEdward Snelson, Zoubin Ghahramani — 2005
• Gaussian Processes for Machine LearningCarl Edward Rasmussen, Christopher K. I. Williams — 2005
• A Unifying View of Sparse Approximate Gaussian Process RegressionJoaquin Quiñonero-Candela, Carl Edward Rasmussen — 2005
• Variational Learning of Inducing Variables in Sparse Gaussian ProcessesMichalis K. Titsias — 2009
• Variational Inference for Latent Variables and Uncertain Inputs in Gaussian ProcessesAndreas Damianou, Michalis K. Titsias, Neil D. Lawrence — 2016