Dimensionality Reduction

Probabilistic Low-Rank Subspace Clustering NeurIPS 2012 Projection Retrieval for Classification NeurIPS 2012 Provable ICA with Unknown Gaussian Noise, with Implications for Gaussian Mixtures and Autoencoders NeurIPS 2012 Semiparametric Principal Component Analysis NeurIPS 2012 Semi-supervised Eigenvectors for Locally-biased Learning NeurIPS 2012 Smooth-projected Neighborhood Pursuit for High-dimensional Nonparanormal Graph Estimation NeurIPS 2012 Sparse Additive Matrix Factorization for Robust PCA and Its Generalization ACML 2012 Sparse Higher-Order Principal Components Analysis AISTATS 2012 Structured Sparse Canonical Correlation Analysis AISTATS 2012 Super-Bit Locality-Sensitive Hashing NeurIPS 2012 Symmetric Correspondence Topic Models for Multilingual Text Analysis NeurIPS 2012 Topographic Analysis of Correlated Components ACML 2012 Toward Understanding Complex Spaces: Graph Laplacians on Manifolds with Singularities and Boundaries COLT 2012 Transelliptical Component Analysis NeurIPS 2012 A Denoising View of Matrix Completion NeurIPS 2011 CAKE: Convex Adaptive Kernel Density Estimation AISTATS 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 Discussion of “Spectral Dimensionality Reduction via Maximum Entropy” AISTATS 2011 Divide-and-Conquer Matrix Factorization NeurIPS 2011 Efficient anomaly detection using bipartite k-NN graphs NeurIPS 2011 k-NN Regression Adapts to Local Intrinsic Dimension NeurIPS 2011