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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 301–310 of 796 papers

TitleStatusHype
A Riemannian gossip approach to decentralized matrix completion—0
Connectivity Shapes Implicit Regularization in Matrix Factorization Models for Matrix Completion—0
Conic Descent and its Application to Memory-efficient Optimization over Positive Semidefinite Matrices—0
Algebraic-Combinatorial Methods for Low-Rank Matrix Completion with Application to Athletic Performance Prediction—0
Guaranteed Matrix Completion via Non-convex Factorization—0
Confidence Intervals of Treatment Effects in Panel Data Models with Interactive Fixed Effects—0
Guaranteed Matrix Completion Under Multiple Linear Transformations—0
A Rank-Corrected Procedure for Matrix Completion with Fixed Basis Coefficients—0
Graph Sampling for Matrix Completion Using Recurrent Gershgorin Disc Shift—0
Graph Regularized Probabilistic Matrix Factorization for Drug-Drug Interactions Prediction—0
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