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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 791–796 of 796 papers

TitleStatusHype
Communication Efficient Parallel Algorithms for Optimization on Manifolds—0
Communication-Efficient Projection-Free Algorithm for Distributed Optimization—0
Community Detection and Matrix Completion with Social and Item Similarity Graphs—0
Completing Any Low-rank Matrix, Provably—0
Completing Low-Rank Matrices with Corrupted Samples from Few Coefficients in General Basis—0
Algebraic-Combinatorial Methods for Low-Rank Matrix Completion with Application to Athletic Performance Prediction—0
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