low-rank approximation

Fitting Low-Rank Tensors in Constant Time NeurIPS 2017 Hierarchically Compositional Kernels for Scalable Nonparametric Learning JMLR 2017 Hyper-Laplacian Regularized Unidirectional Low-Rank Tensor Recovery for Multispectral Image Denoising CVPR 2017 Learning Low-Dimensional Metrics NeurIPS 2017 Memory Efficient Kernel Approximation JMLR 2017 Mixture-Rank Matrix Approximation for Collaborative Filtering NeurIPS 2017 Near Optimal Sketching of Low-Rank Tensor Regression NeurIPS 2017 Nyström Method with Kernel K-means++ Samples as Landmarks ICML 2017 On Compressing Deep Models by Low Rank and Sparse Decomposition CVPR 2017 Online Robust Low-Rank Tensor Learning IJCAI 2017 Predicting Alzheimer's Disease Cognitive Assessment via Robust Low-Rank Structured Sparse Model IJCAI 2017 Radiometric Calibration From Faces in Images CVPR 2017 Riemannian Optimization for Skip-Gram Negative Sampling ACL 2017 Scalable Demand-Aware Recommendation NeurIPS 2017 Storage Fit Learning with Unlabeled Data IJCAI 2017 Tensor Biclustering NeurIPS 2017 Tensor Completion with Side Information: A Riemannian Manifold Approach IJCAI 2017 Analysis of Variational Bayesian Factorizations for Sparse and Low-Rank Estimation ICML 2016 A Simple and Provable Algorithm for Sparse Diagonal CCA ICML 2016 A Subspace Learning Approach for High Dimensional Matrix Decomposition with Efficient Column/Row Sampling ICML 2016 Computationally Efficient Nyström Approximation using Fast Transforms ICML 2016 Doubly Decomposing Nonparametric Tensor Regression ICML 2016 LLORMA: Local Low-Rank Matrix Approximation JMLR 2016 Low-Rank Approximation of Weighted Tree Automata AISTATS 2016 Low-Rank Doubly Stochastic Matrix Decomposition for Cluster Analysis JMLR 2016