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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 21–30 of 158 papers

TitleStatusHype
Randomized Approach to Matrix Completion: Applications in Collaborative Filtering and Image InpaintingCode1
Entry-Specific Bounds for Low-Rank Matrix Completion under Highly Non-Uniform Sampling—0
Effect of Beampattern on Matrix Completion with Sparse Arrays—0
Linear Recursive Feature Machines provably recover low-rank matricesCode1
Efficient Compression of Overparameterized Deep Models through Low-Dimensional Learning DynamicsCode0
Harmonic Retrieval Using Weighted Lifted-Structure Low-Rank Matrix Completion—0
A framework to generate sparsity-inducing regularizers for enhanced low-rank matrix completion—0
Robust Low-Rank Matrix Completion via a New Sparsity-Inducing Regularizer—0
Matrix Completion in Almost-Verification Time—0
Data-based system representations from irregularly measured data—0
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