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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
Discrete Aware Matrix Completion via Convexized _0-Norm Approximation—0
An Extended Frank-Wolfe Method with "In-Face" Directions, and its Application to Low-Rank Matrix Completion—0
Decentralized Singular Value Decomposition for Large-scale Distributed Sensor Networks—0
Low-rank matrix completion theory via Plucker coordinates—0
Advancing Matrix Completion by Modeling Extra Structures beyond Low-Rankness—0
Data-based system representations from irregularly measured data—0
Deep learned SVT: Unrolling singular value thresholding to obtain better MSE—0
Depth Enhancement via Low-rank Matrix Completion—0
A Pre-training Oracle for Predicting Distances in Social Networks—0
A New Retraction for Accelerating the Riemannian Three-Factor Low-Rank Matrix Completion Algorithm—0
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