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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 391–400 of 796 papers

TitleStatusHype
Approximate matrix completion based on cavity method—0
Online Variational Bayesian Subspace Filtering with Applications—0
Generalization error bounds for kernel matrix completion and extrapolation—0
Online Matrix Completion with Side Information—0
Efficiently escaping saddle points on manifolds—0
Inference and Uncertainty Quantification for Noisy Matrix Completion—0
Graphon Estimation from Partially Observed Network DataCode0
Guaranteed Matrix Completion Under Multiple Linear Transformations—0
Implicit Regularization in Deep Matrix FactorizationCode0
Spectral Perturbation Meets Incomplete Multi-view Data—0
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