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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 341–350 of 796 papers

TitleStatusHype
Computing large market equilibria using abstractions—0
Individualized Rank Aggregation using Nuclear Norm Regularization—0
Inductive Collaborative Filtering via Relation Graph Learning—0
Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers—0
Inductive Matrix Completion and Root-MUSIC-Based Channel Estimation for Intelligent Reflecting Surface (IRS)-Aided Hybrid MIMO Systems—0
Going off the Grid: Iterative Model Selection for Biclustered Matrix Completion—0
Computational Limits for Matrix Completion—0
Inductive Pairwise Ranking: Going Beyond the n log(n) Barrier—0
A privacy-preserving distributed credible evidence fusion algorithm for collective decision-making—0
Computational Graph Completion—0
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