dimension reduction

Binary Embedding: Fundamental Limits and Fast Algorithm ICML 2015 Local Ordinal Embedding ICML 2014 Making Fisher Discriminant Analysis Scalable ICML 2014 Multi-label Classification via Feature-aware Implicit Label Space Encoding ICML 2014 New Bounds on Compressive Linear Least Squares Regression AISTATS 2014 Forecastable Component Analysis ICML 2013 Tapkee: An Efficient Dimension Reduction Library JMLR 2013 Topic Discovery through Data Dependent and Random Projections ICML 2013 Gradient-based kernel method for feature extraction and variable selection NeurIPS 2012 Learning Manifolds with K-Means and K-Flats NeurIPS 2012 Minimax Manifold Estimation JMLR 2012 Sparse Higher-Order Principal Components Analysis AISTATS 2012 Super-Bit Locality-Sensitive Hashing NeurIPS 2012 Supervised Dimension Reduction with Topic Models ACML 2012 Unsupervised Multiple Kernel Learning ACML 2011 Learning Gradients: Predictive Models that Infer Geometry and Statistical Dependence JMLR 2010 Penalized Principal Component Regression on Graphs for Analysis of Subnetworks NeurIPS 2010 Supervised Dimension Reduction Using Bayesian Mixture Modeling AISTATS 2010 Compressed Least-Squares Regression NeurIPS 2009 Sparse Metric Learning via Smooth Optimization NeurIPS 2009 A New Algorithm for Estimating the Effective Dimension-Reduction Subspace JMLR 2008 Convergence and Rate of Convergence of a Manifold-Based Dimension Reduction Algorithm NeurIPS 2008 Fast High-dimensional Kernel Summations Using the Monte Carlo Multipole Method NeurIPS 2008 Localized Sliced Inverse Regression NeurIPS 2008 One sketch for all: Theory and Application of Conditional Random Sampling NeurIPS 2008