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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 151–160 of 796 papers

TitleStatusHype
Color Image Recovery Using Generalized Matrix Completion over Higher-Order Finite Dimensional Algebra—0
Data-based system representations from irregularly measured data—0
Imposing Consistency Properties on Blackbox Systems with Applications to SVD-Based Recommender Systems—0
Tackling Combinatorial Distribution Shift: A Matrix Completion Perspective—0
Non-Convex Optimizations for Machine Learning with Theoretical Guarantee: Robust Matrix Completion and Neural Network Learning—0
Graph-Based Matrix Completion Applied to Weather Data—0
Efficient Alternating Minimization with Applications to Weighted Low Rank Approximation—0
Exploiting Observation Bias to Improve Matrix Completion—0
One-sided Matrix Completion from Two Observations Per Row—0
Matrix Completion from General Deterministic Sampling Patterns—0
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