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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 721–730 of 796 papers

TitleStatusHype
Approximate Method of Variational Bayesian Matrix Factorization/Completion with Sparse Prior—0
Approximating Concavely Parameterized Optimization Problems—0
A Pre-training Oracle for Predicting Distances in Social Networks—0
A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery—0
A privacy-preserving distributed credible evidence fusion algorithm for collective decision-making—0
A Proximal Modified Quasi-Newton Method for Nonsmooth Regularized Optimization—0
A Rank-Corrected Procedure for Matrix Completion with Fixed Basis Coefficients—0
1-bit Matrix Completion: PAC-Bayesian Analysis of a Variational Approximation—0
A Riemannian gossip approach to decentralized matrix completion—0
A Riemannian gossip approach to subspace learning on Grassmann manifold—0
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