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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 341–350 of 796 papers

TitleStatusHype
Faster Convergence of Riemannian Stochastic Gradient Descent with Increasing Batch Size—0
Individualized Rank Aggregation using Nuclear Norm Regularization—0
Inductive Collaborative Filtering via Relation Graph Learning—0
A note on the statistical view of matrix completion—0
Inductive Matrix Completion and Root-MUSIC-Based Channel Estimation for Intelligent Reflecting Surface (IRS)-Aided Hybrid MIMO Systems—0
CUR Algorithm with Incomplete Matrix Observation—0
Influenza Modeling Based on Massive Feature Engineering and International Flow Deconvolution—0
Inductive Pairwise Ranking: Going Beyond the n log(n) Barrier—0
Fast Dual-Regularized Autoencoder for Sparse Biological Data—0
Fast Convergence of Langevin Dynamics on Manifold: Geodesics meet Log-Sobolev—0
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