Dimensionality Reduction

Randomized Block Krylov Methods for Stronger and Faster Approximate Singular Value Decomposition NeurIPS 2015 Robust Principal Component Analysis on Graphs ICCV 2015 Scale Up Nonlinear Component Analysis with Doubly Stochastic Gradients NeurIPS 2015 Semi-Supervised Learning With Explicit Relationship Regularization CVPR 2015 Space-Time Local Embeddings NeurIPS 2015 Sparse PCA via Bipartite Matchings NeurIPS 2015 Sparse Submodular Probabilistic PCA AISTATS 2015 Stay on path: PCA along graph paths ICML 2015 Streaming Sparse Principal Component Analysis ICML 2015 Sufficient Dimension Reduction via Direct Estimation of the Gradients of Logarithmic Conditional Densities ACML 2015 Theory of Dual-sparse Regularized Randomized Reduction ICML 2015 A Compact and Discriminative Face Track Descriptor CVPR 2014 A Fast Hierarchical Alternating Least Squares Algorithm for Orthogonal Nonnegative Matrix Factorization ACML 2014 An Information Geometry of Statistical Manifold Learning ICML 2014 A Non-parametric Conditional Factor Regression Model for Multi-Dimensional Input and Response AISTATS 2014 A provable SVD-based algorithm for learning topics in dominant admixture corpus NeurIPS 2014 A statistical model for tensor PCA NeurIPS 2014 Better Feature Tracking Through Subspace Constraints CVPR 2014 Blossom Tree Graphical Models NeurIPS 2014 Clustered factor analysis of multineuronal spike data NeurIPS 2014 Cone-Constrained Principal Component Analysis NeurIPS 2014 Coordinate-descent for learning orthogonal matrices through Givens rotations ICML 2014 Dimensionality Reduction with Subspace Structure Preservation NeurIPS 2014 Discovering Structure in High-Dimensional Data Through Correlation Explanation NeurIPS 2014 Efficient Dimensionality Reduction for High-Dimensional Network Estimation ICML 2014