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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 511–520 of 796 papers

TitleStatusHype
Noisy Inductive Matrix Completion Under Sparse Factor Models—0
Noisy Matrix Completion under Sparse Factor Models—0
Noisy Matrix Completion: Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization—0
Noisy Tensor Completion via the Sum-of-Squares Hierarchy—0
Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data—0
Non-Convex Matrix Completion Against a Semi-Random Adversary—0
Nonconvex Matrix Completion with Linearly Parameterized Factors—0
Nonconvex Optimization Meets Low-Rank Matrix Factorization: An Overview—0
Non-Convex Optimizations for Machine Learning with Theoretical Guarantee: Robust Matrix Completion and Neural Network Learning—0
Nonconvex Rectangular Matrix Completion via Gradient Descent without _2, Regularization—0
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