low-rank approximation

Tight convex relaxations for sparse matrix factorization NeurIPS 2014 Zeta Hull Pursuits: Learning Nonconvex Data Hulls NeurIPS 2014 A New Convex Relaxation for Tensor Completion NeurIPS 2013 Convex Tensor Decomposition via Structured Schatten Norm Regularization NeurIPS 2013 Greedy Bilateral Sketch, Completion & Smoothing AISTATS 2013 Improving CUR Matrix Decomposition and the Nystrom Approximation via Adaptive Sampling JMLR 2013 Large-scale SVD and Manifold Learning JMLR 2013 Learning by Associating Ambiguously Labeled Images CVPR 2013 Low-Rank Matrix and Tensor Completion via Adaptive Sampling NeurIPS 2013 Matrix Completion From any Given Set of Observations NeurIPS 2013 Matrix factorization with binary components NeurIPS 2013 Multi-Task Learning with Gaussian Matrix Generalized Inverse Gaussian Model ICML 2013 Partial Sum Minimization of Singular Values in RPCA for Low-Level Vision ICCV 2013 Revisiting the Nystrom method for improved large-scale machine learning ICML 2013 Robust Low Rank Kernel Embeddings of Multivariate Distributions NeurIPS 2013 Sharp analysis of low-rank kernel matrix approximations COLT 2013 Sparse Additive Text Models with Low Rank Background NeurIPS 2013 Sparse PCA through Low-rank Approximations ICML 2013 Unsupervised Spectral Learning of Finite State Transducers NeurIPS 2013 A Scalable CUR Matrix Decomposition Algorithm: Lower Time Complexity and Tighter Bound NeurIPS 2012 Factoring nonnegative matrices with linear programs NeurIPS 2012 Fast Approximation of Matrix Coherence and Statistical Leverage JMLR 2012 Near-optimal Differentially Private Principal Components NeurIPS 2012 Probabilistic Low-Rank Subspace Clustering NeurIPS 2012 Sampling Methods for the Nyström Method JMLR 2012