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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 201–210 of 796 papers

TitleStatusHype
New and Explicit Constructions of Unbalanced Ramanujan Bipartite Graphs—0
Deterministic Symmetric Positive Semidefinite Matrix Completion—0
A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Embeddings—0
Differentially Private Matrix Completion Revisited—0
Discovering Abstract Symbolic Relations by Learning Unitary Group Representations—0
Discrete Aware Matrix Completion via Convexized _0-Norm Approximation—0
Color Image Inpainting via Robust Pure Quaternion Matrix Completion: Error Bound and Weighted Loss—0
Discrete-Aware Matrix Completion via Proximal Gradient—0
Distributed Matrix Completion and Robust Factorization—0
A Novel Two-Step Method for Cross Language Representation Learning—0
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