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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 751–760 of 796 papers

TitleStatusHype
Practical Matrix Completion and Corruption Recovery using Proximal Alternating Robust Subspace Minimization—0
Manopt, a Matlab toolbox for optimization on manifolds—0
Flexible Low-Rank Statistical Modeling with Side Information—0
On the Predictability of Human Assessment: when Matrix Completion Meets NLP Evaluation—0
Completing Any Low-rank Matrix, Provably—0
R3MC: A Riemannian three-factor algorithm for low-rank matrix completion—0
Provable Inductive Matrix Completion—0
Robust Spectral Compressed Sensing via Structured Matrix Completion—0
Low-rank optimization for distance matrix completion—0
Spectral Compressed Sensing via Structured Matrix Completion—0
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