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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 476–500 of 796 papers

TitleStatusHype
Matrix completion based on Gaussian parameterized belief propagation—0
Matrix Completion, Counterfactuals, and Factor Analysis of Missing Data—0
Matrix Completion for Resolving Label Ambiguity—0
Matrix Completion for Structured Observations—0
Matrix Completion From any Given Set of Observations—0
Matrix Completion from Fewer Entries: Spectral Detectability and Rank Estimation—0
Matrix Completion from General Deterministic Sampling Patterns—0
Matrix Completion from Non-Uniformly Sampled Entries—0
Matrix Completion from O(n) Samples in Linear Time—0
Matrix Completion from Power-Law Distributed Samples—0
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
Matrix Completion of World Trade—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
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
Matrix completion with deterministic pattern - a geometric perspective—0
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