Dimensionality Reduction

Large-Scale Sparse Principal Component Analysis with Application to Text Data NeurIPS 2011 Learning Anchor Planes for Classification NeurIPS 2011 Learning low-rank output kernels ACML 2011 Manifold Precis: An Annealing Technique for Diverse Sampling of Manifolds NeurIPS 2011 Maximum Covariance Unfolding : Manifold Learning for Bimodal Data NeurIPS 2011 Mixed-Variate Restricted Boltzmann Machines ACML 2011 Multilinear Subspace Regression: An Orthogonal Tensor Decomposition Approach NeurIPS 2011 Multi-View Learning of Word Embeddings via CCA NeurIPS 2011 Regularized Laplacian Estimation and Fast Eigenvector Approximation NeurIPS 2011 Sparse Features for PCA-Like Linear Regression NeurIPS 2011 Sparse Manifold Clustering and Embedding NeurIPS 2011 Spectral Dimensionality Reduction via Maximum Entropy AISTATS 2011 Structural equations and divisive normalization for energy-dependent component analysis NeurIPS 2011 The Stationary Subspace Analysis Toolbox JMLR 2011 Unsupervised Multiple Kernel Learning ACML 2011 Variational Gaussian Process Dynamical Systems NeurIPS 2011 An Inverse Power Method for Nonlinear Eigenproblems with Applications in 1-Spectral Clustering and Sparse PCA NeurIPS 2010 CUR from a Sparse Optimization Viewpoint NeurIPS 2010 Efficient Optimization for Discriminative Latent Class Models NeurIPS 2010 Estimation of Rényi Entropy and Mutual Information Based on Generalized Nearest-Neighbor Graphs NeurIPS 2010 Fast detection of multiple change-points shared by many signals using group LARS NeurIPS 2010 Fluid Dynamics Models for Low Rank Discriminant Analysis AISTATS 2010 Generative Local Metric Learning for Nearest Neighbor Classification NeurIPS 2010 Humans Learn Using Manifolds, Reluctantly NeurIPS 2010 Inference of Sparse Networks with Unobserved Variables. Application to Gene Regulatory Networks AISTATS 2010