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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 451–500 of 796 papers

TitleStatusHype
Simple Heuristics Yield Provable Algorithms for Masked Low-Rank Approximation—0
Low-Rank Approximations of Nonseparable Panel Models—0
Low-rank Bayesian matrix completion via geodesic Hamiltonian Monte Carlo on Stiefel manifolds—0
Low-Rank Covariance Completion for Graph Quilting with Applications to Functional Connectivity—0
Low-rank matrix completion and denoising under Poisson noise—0
Low rank matrix completion and realization of graphs: results and problems—0
Low Rank Matrix Completion via Robust Alternating Minimization in Nearly Linear Time—0
Low Rank Matrix Completion with Exponential Family Noise—0
Low-rank matrix recovery with composite optimization: good conditioning and rapid convergence—0
Low-rank matrix recovery with non-quadratic loss: projected gradient method and regularity projection oracle—0
Low-Rank Modeling and Its Applications in Image Analysis—0
Low-rank optimization for distance matrix completion—0
Low-rank optimization with trace norm penalty—0
Low Rank Quaternion Matrix Completion Based on Quaternion QR Decomposition and Sparse Regularizer—0
Low Rank Quaternion Matrix Recovery via Logarithmic Approximation—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
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
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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