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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 271–280 of 796 papers

TitleStatusHype
Matrix Completion with Model-free Weighting—0
A Pre-training Oracle for Predicting Distances in Social Networks—0
Patch Tracking-based Streaming Tensor Ring Completion for Visual Data Recovery—0
Multi-source Learning via Completion of Block-wise Overlapping Noisy Matrices—0
Low-Rank Hankel Tensor Completion for Traffic Speed EstimationCode0
Sharp Restricted Isometry Property Bounds for Low-rank Matrix Recovery Problems with Corrupted Measurements—0
Deep learned SVT: Unrolling singular value thresholding to obtain better MSE—0
On the Optimality of Nuclear-norm-based Matrix Completion for Problems with Smooth Non-linear Structure—0
Implicit Regularization in Deep Tensor Factorization—0
Nonparametric Trace Regression in High Dimensions via Sign Series Representation—0
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