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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 41–50 of 158 papers

TitleStatusHype
Discrete Aware Matrix Completion via Convexized _0-Norm Approximation—0
A privacy-preserving distributed credible evidence fusion algorithm for collective decision-making—0
Data-based system representations from irregularly measured data—0
Effect of Beampattern on Matrix Completion with Sparse Arrays—0
Efficient Alternating Minimization with Applications to Weighted Low Rank Approximation—0
A Riemannian gossip approach to subspace learning on Grassmann manifold—0
A New Retraction for Accelerating the Riemannian Three-Factor Low-Rank Matrix Completion Algorithm—0
Communication Efficient Parallel Algorithms for Optimization on Manifolds—0
Efficient Low-Rank Matrix Factorization based on l1,ε-norm for Online Background Subtraction—0
Relative Error Bound Analysis for Nuclear Norm Regularized Matrix Completion—0
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