Dimensionality Reduction

Inferring Stimulus Selectivity from the Spatial Structure of Neural Network Dynamics NeurIPS 2010 LSTD with Random Projections NeurIPS 2010 Penalized Principal Component Regression on Graphs for Analysis of Subnetworks NeurIPS 2010 Random Projections for k-means Clustering NeurIPS 2010 Random Projection Trees Revisited NeurIPS 2010 Robust PCA via Outlier Pursuit NeurIPS 2010 Sample Complexity of Testing the Manifold Hypothesis NeurIPS 2010 Simple Exponential Family PCA AISTATS 2010 Sufficient Dimension Reduction via Squared-loss Mutual Information Estimation AISTATS 2010 Unsupervised Kernel Dimension Reduction NeurIPS 2010 Worst-Case Linear Discriminant Analysis NeurIPS 2010 Compressed Least-Squares Regression NeurIPS 2009 Heavy-Tailed Symmetric Stochastic Neighbor Embedding NeurIPS 2009 Learning transport operators for image manifolds NeurIPS 2009 Locality-sensitive binary codes from shift-invariant kernels NeurIPS 2009 Manifold Regularization for SIR with Rate Root-n Convergence NeurIPS 2009 Multi-Label Prediction via Sparse Infinite CCA NeurIPS 2009 Probabilistic Relational PCA NeurIPS 2009 Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Matrices via Convex Optimization NeurIPS 2009 Semi-supervised Regression using Hessian energy with an application to semi-supervised dimensionality reduction NeurIPS 2009 Toward Provably Correct Feature Selection in Arbitrary Domains NeurIPS 2009 Variational Gaussian-process factor analysis for modeling spatio-temporal data NeurIPS 2009 Bayesian Exponential Family PCA NeurIPS 2008 Convergence and Rate of Convergence of a Manifold-Based Dimension Reduction Algorithm NeurIPS 2008 Covariance Estimation for High Dimensional Data Vectors Using the Sparse Matrix Transform NeurIPS 2008