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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 61–70 of 796 papers

TitleStatusHype
Robust Egoistic Rigid Body Localization—0
Low rank matrix completion and realization of graphs: results and problems—0
Improved Approximation Algorithms for Low-Rank Problems Using Semidefinite Optimization—0
Deeply Learned Robust Matrix Completion for Large-scale Low-rank Data Recovery—0
Robust Matrix Completion for Discrete Rating-Scale DataCode0
Matrix Completion via Residual Spectral Matching—0
Representational Transfer Learning for Matrix Completion—0
A privacy-preserving distributed credible evidence fusion algorithm for collective decision-making—0
On adaptivity and minimax optimality of two-sided nearest neighbors—0
Efficient and Robust Freeway Traffic Speed Estimation under Oblique Grid using Vehicle Trajectory DataCode0
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