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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 141–150 of 796 papers

TitleStatusHype
Communication-Efficient Projection-Free Algorithm for Distributed Optimization—0
Approximate Method of Variational Bayesian Matrix Factorization/Completion with Sparse Prior—0
Completing Any Low-rank Matrix, Provably—0
Completing Low-Rank Matrices with Corrupted Samples from Few Coefficients in General Basis—0
Communication Efficient Parallel Algorithms for Optimization on Manifolds—0
Combining Explicit and Implicit Regularization for Efficient Learning in Deep Networks—0
Approximate matrix completion based on cavity method—0
Computational Graph Completion—0
Computational Limits for Matrix Completion—0
Adaptively-weighted Nearest Neighbors for Matrix Completion—0
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