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Linear Algebra

480 directly classified papers

Papers per year

Papers

Global Optimality of Local Search for Low Rank Matrix Recovery NIPS 2016
Quadratic Optimization with Orthogonality Constraints: Explicit Lojasiewicz Exponent and Linear Convergence of Line-Search Methods ICML 2016
How to Fake Multiply by a Gaussian Matrix ICML 2016
Correlated-PCA: Principal Components' Analysis when Data and Noise are Correlated NIPS 2016
Learning from Multiway Data: Simple and Efficient Tensor Regression ICML 2016
Fast recovery from a union of subspaces NIPS 2016
SPSD Matrix Approximation vis Column Selection: Theories, Algorithms, and Extensions JMLR 2016
Low-Rank Approximation of Weighted Tree Automata AISTATS 2016
Low-Rank Doubly Stochastic Matrix Decomposition for Cluster Analysis JMLR 2016
Towards More Efficient SPSD Matrix Approximation and CUR Matrix Decomposition JMLR 2016
A Subspace Learning Approach for High Dimensional Matrix Decomposition with Efficient Column/Row Sampling ICML 2016
Coordinate-wise Power Method NIPS 2016
Principal Component Projection Without Principal Component Analysis ICML 2016
Clustering Signed Networks with the Geometric Mean of Laplacians NIPS 2016
Robust Spectral Detection of Global Structures in the Data by Learning a Regularization NIPS 2016
Single Pass PCA of Matrix Products NIPS 2016
Matrix Eigen-decomposition via Doubly Stochastic Riemannian Optimization ICML 2016
Faster Eigenvector Computation via Shift-and-Invert Preconditioning ICML 2016
Tensor Decomposition via Joint Matrix Schur Decomposition ICML 2016
An Improved Gap-Dependency Analysis of the Noisy Power Method COLT 2016
Linear Shape Deformation Models With Local Support Using Graph-Based Structured Matrix Factorisation CVPR 2016
Multiresolution Matrix Compression AISTATS 2016
Tensor vs. Matrix Methods: Robust Tensor Decomposition under Block Sparse Perturbations AISTATS 2016
Low-rank Solutions of Linear Matrix Equations via Procrustes Flow ICML 2016
A posteriori error bounds for joint matrix decomposition problems NIPS 2016