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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 91–100 of 158 papers

TitleStatusHype
Leave-One-Out Analysis for Nonconvex Robust Matrix Completion with General Thresholding Functions—0
Simple Heuristics Yield Provable Algorithms for Masked Low-Rank Approximation—0
Low-Rank Covariance Completion for Graph Quilting with Applications to Functional Connectivity—0
Low-rank matrix completion and denoising under Poisson noise—0
Low rank matrix completion and realization of graphs: results and problems—0
Low Rank Matrix Completion via Robust Alternating Minimization in Nearly Linear Time—0
Low Rank Matrix Completion with Exponential Family Noise—0
Low-rank optimization with trace norm penalty—0
Low-tubal-rank Tensor Completion using Alternating Minimization—0
Manopt, a Matlab toolbox for optimization on manifolds—0
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