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The Gaussian process latent variable model (GP-LVM) provides a flexible approach for non-linear dimensionality reduction that has been widely applied.
A nonlinear mapping for data structure analysis
John W. Sammon · 1969
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Multivariate analysis
Kantilal V. Mardia, John T. Kent, and John M. Bibby · 1979
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Latent Variable Models and Factor Analysis
David J. Bartholomew · 1987
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Analysis of multiphase flows using dual-energy gamma densitometry and neural networks
Christopher M. Bishop and Gwilym D. James · 1993
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Novel approach to nonlinear/non-Gaussian Bayesian state estimation
Neil J. Gordon, David J. Salmond, and Adrian F. M. Smith · 1993
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Statistical Factor Analysis and Related Methods
Alexander Basilevsky · 1994
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Bayesian neural networks and density networks
David J. C. MacKay · 1995
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GTM: the Generative Topographic Mapping
Christopher M. Bishop, Marcus Svensén, and Christopher K. I. Williams · 1998
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Regression with input-dependent noise: A Gaussian process treatment
Paul W. Goldberg, Christopher K. I. Williams, and Christopher M. Bishop · 1998
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Advances in Neural Information Processing Systems , volume 10, Cambridge, MA, 1998. MIT Press
Michael I. Jordan, Michael J. Kearns, and Sara A. Solla, editors · 1998
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Introduction to Gaussian processes
D. J. C. MacKay · 1998
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EM algorithms for PCA and SPCA
Sam T. Roweis · 1998
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Nonlinear component analysis as a kernel eigenvalue problem
Bernhard Schölkopf, Alexander Smola, and Klaus-Robert Müller · 1998
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Bayesian PCA
Christopher M. Bishop · 1999
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Probabilistic principal component analysis
Michael E. Tipping and Christopher M. Bishop · 1999
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Nonlinear dimensionality reduction by locally linear embedding
Sam T. Roweis and Lawrence K. Saul · 2000
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A global geometric framework for nonlinear dimensionality reduction
Joshua B. Tenenbaum, Virginia de Silva, and John C. Langford · 2000
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Independent Component Analysis
Aapo Hyvärinen, Juha Karhunen, and Erkki Oja · 2001
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Advances in Neural Information Processing Systems , volume 13, Cambridge, MA, 2001. MIT Press
Todd K. Leen, Thomas G. Dietterich, and Volker Tresp, editors · 2001
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Automatic choice of dimensionality for PCA
Thomas P. Minka · 2001
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Sparse greedy Gaussian process regression
Alexander J. Smola and Peter L. Bartlett · 2001
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Gaussian Processes — Iterative Sparse Approximations
Lehel Csató · 2002
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Sparse on-line Gaussian processes
Lehel Csató and Manfred Opper · 2002
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Bayesian inference for the uncertainty distribution of computer model outputs
Jeremey Oakley and Anthony O’Hagan · 2002
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Laplacian eigenmaps for dimensionality reduction and data representation
Mikhail Belkin and Partha Niyogi · 2003
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Gaussian process priors with uncertain inputs—application to multiple-step ahead time series forecasting
Agathe Girard, Carl Edward Rasmussen, Joaquin Quiñonero Candela, and Roderick Murray-Smith · 2003
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Propagation of uncertainty in bayesian kernel models-application to multiple-step ahead forecasting
Joaquin Quiñonero-Candela, Agathe Girard, Jan Larsen, and Carl Edward Rasmussen · 2003
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Fast forward selection to speed up sparse Gaussian process regression
Matthias Seeger, Christopher K. I. Williams, and Neil D. Lawrence · 2003
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Modeling human motion using binary latent variables
Graham W. Taylor, Geoffrey E. Hinton, and Sam Roweis · 2007
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Discriminative Gaussian process latent variable model for classification
Raquel Urtasun and Trevor Darrell · 2007
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Gaussian process dynamical models for human motion
Jack M. Wang, David J. Fleet, and Aaron Hertzmann · 2007
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Gaussian process latent variable models for human pose estimation
Carl Henrik Ek, Philip H.S. Torr, and Neil D. Lawrence · 2008
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Visualizing data using t-SNE
Larens J. P. van der Maaten and Geoffrey E. Hinton · 2008
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The variational Gaussian approximation revisited
Manfred Opper and Cédric Archambeau · 2009
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Gaussian process models for visualisation of high dimensional data
Neil D. Lawrence · 2004
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Warped Gaussian processes
Edward Snelson, Carl Edward Rasmussen, and Zoubin Ghahramani · 2004
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Probabilistic non-linear principal component analysis with Gaussian process latent variable models
Neil D. Lawrence · 2005
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A unifying view of sparse approximate Gaussian process regression
Joaquin Quiñonero Candela and Carl Edward Rasmussen · 2005
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Semi-supervised self-training of object detection models
C. Rosenberg, M. Hebert, and H. Schneiderman · 2005
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Pattern Recognition and Machine Learning
Christopher M. Bishop · 2006
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Fast variational inference for Gaussian Process models through KL-correction
Nathaniel J. King and Neil D. Lawrence · 2006
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Variational Gaussian process dynamical systems
Andreas Damianou, Michalis K. Titsias, and Neil D. Lawrence · 2011
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Learning GP-Bayesfilters via Gaussian process latent variable models
Jonathan Ko and Dieter Fox · 2011
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Variational heteroscedastic gaussian process regression
Miguel Lázaro-Gredilla and Michalis K. Titsias · 2011
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Gaussian process training with input noise
Andrew McHutchon and Carl Edward Rasmussen · 2011
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Manifold relevance determination
Andreas Damianou, Carl Henrik Ek, Michalis K. Titsias, and Neil D. Lawrence · 2012
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Robust filtering and smoothing with Gaussian processes
Marc Peter Deisenroth, Ryan Darby Turner, Marco F Huber, Uwe D Hanebeck, and Carl Edward Rasmussen · 2012
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A unifying probabilistic perspective for spectral dimensionality reduction: Insights and new models
Neil D. Lawrence · 2012
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Bayesian warped Gaussian processes
Miguel Lázaro-Gredilla · 2012
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Deep Gaussian processes
Andreas Damianou and Neil D. Lawrence · 2013
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Detecting regulatory gene-environment interactions with unmeasured environmental factors
Nicoló Fusi, Christoph Lippert, Karsten Borgwardt, Neil D. Lawrence, and Oliver Stegle · 2013
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Auto-encoding variational Bayes
Diederik P Kingma and Max Welling · 2013
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Bayesian Filtering and Smoothing
Simo Särkkä · 2013
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Variational inference for mahalanobis distance metrics in Gaussian process regression
Michalis Titsias and Miguel Lázaro-Gredilla · 2013
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Identification of Gaussian process state-space models with particle stochastic approximation em
Roger Frigola, Fredrik Lindsten, Thomas B Schön, and Carl E Rasmussen · 2014
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Distributed variational inference in sparse Gaussian process regression and latent variable models
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