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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 376–400 of 796 papers

TitleStatusHype
Leave-One-Out Analysis for Nonconvex Robust Matrix Completion with General Thresholding Functions—0
Lifelong Matrix Completion with Sparsity-Number—0
Active Feature Acquisition with Supervised Matrix Completion—0
Link Discovery using Graph Feature Tracking—0
Link Prediction via Matrix Completion—0
Local Search Algorithms for Rank-Constrained Convex Optimization—0
Log-Normal Matrix Completion for Large Scale Link Prediction—0
Sum-of-squares meets square loss: Fast rates for agnostic tensor completion—0
Low Permutation-rank Matrices: Structural Properties and Noisy Completion—0
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
SUPER-Rec: SUrrounding Position-Enhanced Representation for Recommendation—0
Survey of Matrix Completion Algorithms—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
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