dimensionality reduction

Toward Understanding Complex Spaces: Graph Laplacians on Manifolds with Singularities and Boundaries COLT 2012 Transelliptical Component Analysis NeurIPS 2012 A Cure for Variance Inflation in High Dimensional Kernel Principal Component Analysis JMLR 2011 Data Skeletonization via Reeb Graphs NeurIPS 2011 Demixed Principal Component Analysis NeurIPS 2011 Dimensionality Reduction for Spectral Clustering AISTATS 2011 Dimensionality Reduction Using the Sparse Linear Model NeurIPS 2011 Directed Graph Embedding: an Algorithm based on Continuous Limits of Laplacian-type Operators NeurIPS 2011 Large-Scale Sparse Principal Component Analysis with Application to Text Data NeurIPS 2011 Learning Anchor Planes for Classification NeurIPS 2011 Learning Sparse Representations of High Dimensional Data on Large Scale Dictionaries NeurIPS 2011 Locally Defined Principal Curves and Surfaces JMLR 2011 Maximum Covariance Unfolding : Manifold Learning for Bimodal Data NeurIPS 2011 Mixed-Variate Restricted Boltzmann Machines ACML 2011 Sparse Features for PCA-Like Linear Regression NeurIPS 2011 Sparse Manifold Clustering and Embedding NeurIPS 2011 The Stationary Subspace Analysis Toolbox JMLR 2011 Unsupervised Multiple Kernel Learning ACML 2011 Variational Gaussian Process Dynamical Systems NeurIPS 2011 A highly efficient blocked Gibbs sampler reconstruction of multidimensional NMR spectra AISTATS 2010 An Inverse Power Method for Nonlinear Eigenproblems with Applications in 1-Spectral Clustering and Sparse PCA NeurIPS 2010 Bayesian Gaussian Process Latent Variable Model AISTATS 2010 CUR from a Sparse Optimization Viewpoint NeurIPS 2010 Efficient Optimization for Discriminative Latent Class Models NeurIPS 2010 Fluid Dynamics Models for Low Rank Discriminant Analysis AISTATS 2010