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Low-Rank Matrix Completion

Low-Rank Matrix Completion is an important problem with several applications in areas such as recommendation systems, sketching, and quantum tomography. The goal in matrix completion is to recover a low rank matrix, given a small number of entries of the matrix.

Source: Universal Matrix Completion

Papers

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Showing 111–120 of 158 papers

TitleStatusHype
Modelling Competitive Sports: Bradley-Terry-Élő Models for Supervised and On-Line Learning of Paired Competition Outcomes—0
High-Rank Matrix Completion and Clustering under Self-Expressive Models—0
Asynchronous Parallel Learning for Neural Networks and Structured Models with Dense Features—0
Low-tubal-rank Tensor Completion using Alternating Minimization—0
Nearly-optimal Robust Matrix Completion—0
Riemannian stochastic variance reduced gradient on Grassmann manifoldCode0
Depth Image Inpainting: Improving Low Rank Matrix Completion with Low Gradient RegularizationCode0
Scaled stochastic gradient descent for low-rank matrix completion—0
Secrets of Matrix Factorization: Approximations, Numerics, Manifold Optimization and Random Restarts—0
Collaborative Filtering with Graph Information: Consistency and Scalable MethodsCode0
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