manifold learning

Learning Gradients: Predictive Models that Infer Geometry and Statistical Dependence JMLR 2010 Locally Linear Denoising on Image Manifolds AISTATS 2010 Random Projection Trees Revisited NeurIPS 2010 Sample Complexity of Testing the Manifold Hypothesis NeurIPS 2010 Fast Graph Laplacian Regularized Kernel Learning via Semidefinite–Quadratic–Linear Programming NeurIPS 2009 Learning transport operators for image manifolds NeurIPS 2009 Nonlinear Learning using Local Coordinate Coding NeurIPS 2009 Semi-supervised Regression using Hessian energy with an application to semi-supervised dimensionality reduction NeurIPS 2009 Convergence and Rate of Convergence of a Manifold-Based Dimension Reduction Algorithm NeurIPS 2008 Diffeomorphic Dimensionality Reduction NeurIPS 2008 Manifold Learning: The Price of Normalization JMLR 2008 Non-parametric Regression Between Manifolds NeurIPS 2008 Regularized Learning with Networks of Features NeurIPS 2008 Spectral Hashing NeurIPS 2008 Colored Maximum Variance Unfolding NeurIPS 2007 Graph Laplacians and their Convergence on Random Neighborhood Graphs JMLR 2007 Iterative Non-linear Dimensionality Reduction with Manifold Sculpting NeurIPS 2007 Learning the 2-D Topology of Images NeurIPS 2007 Learning the structure of manifolds using random projections NeurIPS 2007 People Tracking with the Laplacian Eigenmaps Latent Variable Model NeurIPS 2007 Random Projections for Manifold Learning NeurIPS 2007 Semi-Supervised Multitask Learning NeurIPS 2007 Convergence of Laplacian Eigenmaps NeurIPS 2006 Greedy Layer-Wise Training of Deep Networks NeurIPS 2006 Learning on Graph with Laplacian Regularization NeurIPS 2006