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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 401–450 of 796 papers

TitleStatusHype
Low Rank Quaternion Matrix Recovery via Logarithmic Approximation—0
Widely Separated MIMO Radar Using Matrix Completion—0
Low-tubal-rank Tensor Completion using Alternating Minimization—0
LRSVRG-IMC: An SVRG-Based Algorithm for LowRank Inductive Matrix Completion—0
Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion—0
Machine Learning Methods Economists Should Know About—0
Manopt, a Matlab toolbox for optimization on manifolds—0
Matrix Co-completion for Multi-label Classification with Missing Features and Labels—0
Matrix Coherence and the Nystrom Method—0
Matrix completion and extrapolation via kernel regression—0
A Comparison of Clustering and Missing Data Methods for Health Sciences—0
Matrix Completion and Performance Guarantees for Single Individual Haplotyping—0
Matrix Completion and Related Problems via Strong Duality—0
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
Symmetric Matrix Completion with ReLU Sampling—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
Symmetric Tensor Completion from Multilinear Entries and Learning Product Mixtures over the Hypercube—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
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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