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Matrix Completion

Matrix Completion is a method for recovering lost information. It originates from machine learning and usually deals with highly sparse matrices. Missing or unknown data is estimated using the low-rank matrix of the known data.

Source: A Fast Matrix-Completion-Based Approach for Recommendation Systems

Papers

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Showing 451–460 of 796 papers

TitleStatusHype
Simple Heuristics Yield Provable Algorithms for Masked Low-Rank Approximation—0
Low-Rank Approximations of Nonseparable Panel Models—0
Low-rank Bayesian matrix completion via geodesic Hamiltonian Monte Carlo on Stiefel manifolds—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 matrix recovery with composite optimization: good conditioning and rapid convergence—0
Low-rank matrix recovery with non-quadratic loss: projected gradient method and regularity projection oracle—0
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