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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 651–660 of 796 papers

TitleStatusHype
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
Riemannian Stochastic Proximal Gradient Methods for Nonsmooth Optimization over the Stiefel Manifold—0
Riemannian stochastic quasi-Newton algorithm with variance reduction and its convergence analysis—0
Truncated Nuclear Norm Minimization for Image Restoration Based On Iterative Support Detection—0
Two-snapshot DOA Estimation via Hankel-structured Matrix Completion—0
Robust Egoistic Rigid Body Localization—0
Robust Low-rank Matrix Completion via an Alternating Manifold Proximal Gradient Continuation Method—0
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