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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 671–680 of 796 papers

TitleStatusHype
Regularizing Autoencoder-Based Matrix Completion Models via Manifold Learning—0
Relax and Randomize : From Value to Algorithms—0
Relaxed Leverage Sampling for Low-rank Matrix Completion—0
Relevance Singular Vector Machine for low-rank matrix sensing—0
Removing Clouds and Recovering Ground Observations in Satellite Image Sequences via Temporally Contiguous Robust Matrix Completion—0
Representational Transfer Learning for Matrix Completion—0
Representation learning of drug and disease terms for drug repositioning—0
RGNMR: A Gauss-Newton method for robust matrix completion with theoretical guarantees—0
Riemannian Optimization for Non-convex Euclidean Distance Geometry with Global Recovery Guarantees—0
Riemannian Perspective on Matrix Factorization—0
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