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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 61–70 of 158 papers

TitleStatusHype
Fast Low-Rank Bayesian Matrix Completion with Hierarchical Gaussian Prior Models—0
Fixed-rank matrix factorizations and Riemannian low-rank optimization—0
A framework to generate sparsity-inducing regularizers for enhanced low-rank matrix completion—0
Adaptive Noisy Matrix Completion—0
Depth Enhancement via Low-rank Matrix Completion—0
Deep learned SVT: Unrolling singular value thresholding to obtain better MSE—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
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