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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 261–270 of 796 papers

TitleStatusHype
A Unified Framework for Structured Low-rank Matrix Learning—0
Deep geometric matrix completion: Are we doing it right?—0
A Unified Framework for Sparse Relaxed Regularized Regression: SR3—0
A majorization-minimization algorithm for nonnegative binary matrix factorization—0
A Unified Convex Surrogate for the Schatten-p Norm—0
Decentralized Singular Value Decomposition for Large-scale Distributed Sensor Networks—0
Decentralized Frank-Wolfe Algorithm for Convex and Non-convex Problems—0
A Unified Computational and Statistical Framework for Nonconvex Low-Rank Matrix Estimation—0
Always Valid Risk Monitoring for Online Matrix Completion—0
A divide-and-conquer algorithm for binary matrix completion—0
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