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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 Nonconvex Regularization: Spectral Operators and Scalable Algorithms—0
Indian Regional Movie Dataset for Recommender SystemsCode0
CAYLEYNETS: SPECTRAL GRAPH CNNS WITH COMPLEX RATIONAL FILTERS—0
Differentially Private Matrix Completion Revisited—0
An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic ControlsCode0
On Deterministic Sampling Patterns for Robust Low-Rank Matrix Completion—0
A New Theory for Matrix Completion—0
Thy Friend is My Friend: Iterative Collaborative Filtering for Sparse Matrix Estimation—0
Matrix Norm Estimation from a Few EntriesCode0
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion, and Blind Deconvolution—0
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