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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 426–450 of 796 papers

TitleStatusHype
Matrix Completion has No Spurious Local Minimum—0
Matrix Completion in Almost-Verification Time—0
Matrix Completion-Informed Deep Unfolded Equilibrium Models for Self-Supervised k-Space Interpolation in MRI—0
Matrix Completion in Group Testing: Bounds and Simulations—0
Synthesis of Sparse Linear Arrays via Low-Rank Hankel Matrix Completion—0
Matrix Completion of World Trade—0
Tackling Combinatorial Distribution Shift: A Matrix Completion Perspective—0
Matrix Completion under Interval Uncertainty—0
Matrix Completion under Low-Rank Missing Mechanism—0
Matrix Completion Under Monotonic Single Index Models—0
Matrix Completion via Factorizing Polynomials—0
Matrix Completion via Max-Norm Constrained Optimization—0
Tailed Low-Rank Matrix Factorization for Similarity Matrix Completion—0
Matrix Completion via Non-Convex Relaxation and Adaptive Correlation Learning—0
Matrix Completion via Nonsmooth Regularization of Fully Connected Neural Networks—0
Matrix Completion via Residual Spectral Matching—0
Matrix completion with column manipulation: Near-optimal sample-robustness-rank tradeoffs—0
A Characterization of Deterministic Sampling Patterns for Low-Rank Matrix Completion—0
Taming the Wild: A Unified Analysis of Hogwild!-Style Algorithms—0
Matrix completion with deterministic pattern - a geometric perspective—0
Matrix Completion with Graph Information: A Provable Nonconvex Optimization Approach—0
Matrix Completion with Heterogonous Cost—0
Matrix Completion with Hierarchical Graph Side Information—0
Matrix Completion with Hypergraphs:Sharp Thresholds and Efficient Algorithms—0
Matrix Completion with Model-free Weighting—0
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