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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 221–230 of 796 papers

TitleStatusHype
Efficient Low-Rank Matrix Factorization based on l1,ε-norm for Online Background Subtraction—0
A Scalable, Adaptive and Sound Nonconvex Regularizer for Low-rank Matrix Completion—0
Efficient Low Rank Tensor Ring Completion—0
Efficiently escaping saddle points on manifolds—0
Efficient MCMC Sampling for Bayesian Matrix Factorization by Breaking Posterior Symmetries—0
Color Image Inpainting via Robust Pure Quaternion Matrix Completion: Error Bound and Weighted Loss—0
Bounded Manifold Completion—0
A Comparison of Clustering and Missing Data Methods for Health Sciences—0
Efficient Pairwise Learning Using Kernel Ridge Regression: an Exact Two-Step Method—0
A Novel Two-Step Method for Cross Language Representation Learning—0
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