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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 501–510 of 796 papers

TitleStatusHype
Matrix Completion with Graph Information: A Provable Nonconvex Optimization Approach—0
Matrix Completion with Heterogonous Cost—0
Matrix Completion with Hierarchical Graph Side Information—0
Matrix Completion with Hypergraphs:Sharp Thresholds and Efficient Algorithms—0
Matrix Completion with Model-free Weighting—0
Leveraged Matrix Completion with Noise—0
Matrix Completion with Noisy Entries and Outliers—0
Matrix Completion with Noisy Side Information—0
Matrix Completion with Nonconvex Regularization: Spectral Operators and Scalable Algorithms—0
Matrix completion with queries—0
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