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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 Characterization of Deterministic Sampling Patterns for Low-Rank Matrix Completion—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
A Block Lanczos with Warm Start Technique for Accelerating Nuclear Norm Minimization Algorithms—0
Deep Non-Rigid Structure from Motion with Missing Data—0
Background Subtraction via Fast Robust Matrix Completion—0
Deeply Learned Robust Matrix Completion for Large-scale Low-rank Data Recovery—0
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