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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 31–40 of 158 papers

TitleStatusHype
Harmonic Retrieval Using Weighted Lifted-Structure Low-Rank Matrix Completion—0
Efficient Compression of Overparameterized Deep Models through Low-Dimensional Learning DynamicsCode0
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
Non-Convex Optimizations for Machine Learning with Theoretical Guarantee: Robust Matrix Completion and Neural Network Learning—0
Graph-Based Matrix Completion Applied to Weather Data—0
Efficient Alternating Minimization with Applications to Weighted Low Rank Approximation—0
Matrix Completion from General Deterministic Sampling Patterns—0
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