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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 551–560 of 796 papers

TitleStatusHype
Exact tensor completion with sum-of-squares—0
SAR: Semantic Analysis for Recommendation—0
Riemannian stochastic variance reduced gradient algorithm with retraction and vector transportCode0
Learning from Ambiguously Labeled Face Images—0
Mutual Kernel Matrix Completion—0
Inductive Pairwise Ranking: Going Beyond the n log(n) Barrier—0
Matrix Completion from O(n) Samples in Linear Time—0
Solving Uncalibrated Photometric Stereo Using Fewer Images by Jointly Optimizing Low-rank Matrix Completion and Integrability—0
Modelling Competitive Sports: Bradley-Terry-Élő Models for Supervised and On-Line Learning of Paired Competition Outcomes—0
Deterministic and Probabilistic Conditions for Finite Completability of Low-rank Multi-View Data—0
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