Sparse Gaussian processes with manifold-preserving graph reduction

[1]  Robin Zapotocky.  Carl , 2014 .

[2]  Shiliang Sun,et al.  Manifold-preserving graph reduction for sparse semi-supervised learning , 2014, Neurocomputing.

[3]  Shiliang Sun,et al.  Single-task and multitask sparse Gaussian processes , 2013, 2013 International Conference on Machine Learning and Cybernetics.

[4]  Shiliang Sun,et al.  Infinite mixtures of multivariate Gaussian processes , 2013, 2013 International Conference on Machine Learning and Cybernetics.

[5]  Shiliang Sun,et al.  A review of deterministic approximate inference techniques for Bayesian machine learning , 2013, Neural Computing and Applications.

[6]  Bernie Wang,et al.  Sparse Gaussian Processes for Multi-task Learning , 2012, ECML/PKDD.

[7]  Shiliang Sun,et al.  Multi-view Laplacian Support Vector Machines , 2011, ADMA.

[8]  Michalis K. Titsias,et al.  Variational Learning of Inducing Variables in Sparse Gaussian Processes , 2009, AISTATS.

[9]  Mikhail Belkin,et al.  Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples , 2006, J. Mach. Learn. Res..

[10]  Zoubin Ghahramani,et al.  Sparse Gaussian Processes using Pseudo-inputs , 2005, NIPS.

[11]  Carl E. Rasmussen,et al.  A Unifying View of Sparse Approximate Gaussian Process Regression , 2005, J. Mach. Learn. Res..

[12]  Neil D. Lawrence,et al.  Learning to learn with the informative vector machine , 2004, ICML.

[13]  Mikhail Belkin,et al.  Laplacian Eigenmaps for Dimensionality Reduction and Data Representation , 2003, Neural Computation.

[14]  Neil D. Lawrence,et al.  Fast Sparse Gaussian Process Methods: The Informative Vector Machine , 2002, NIPS.

[15]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[16]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[17]  Christopher K. I. Williams,et al.  Using the Nyström Method to Speed Up Kernel Machines , 2000, NIPS.