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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 521–530 of 796 papers

TitleStatusHype
Leave-one-out Approach for Matrix Completion: Primal and Dual Analysis—0
Nonlinear Inductive Matrix Completion based on One-layer Neural Networks—0
Nonlinear Traffic Prediction as a Matrix Completion Problem with Ensemble Learning—0
Patch Tracking-based Streaming Tensor Ring Completion for Visual Data Recovery—0
Non-Local Robust Quaternion Matrix Completion for Color Images and Videos Inpainting—0
The Noisy Power Method: A Meta Algorithm with Applications—0
Nonparametric Estimation of Low Rank Matrix Valued Function—0
Nonparametric Trace Regression in High Dimensions via Sign Series Representation—0
Norm-Bounded Low-Rank Adaptation—0
No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified Geometric Analysis—0
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