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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 251–260 of 796 papers

TitleStatusHype
Extended Gauss-Newton and ADMM-Gauss-Newton Algorithms for Low-Rank Matrix Optimization—0
Factor Group-Sparse Regularization for Efficient Low-Rank Matrix Recovery—0
Factorization Approach for Low-complexity Matrix Completion Problems: Exponential Number of Spurious Solutions and Failure of Gradient Methods—0
Factorizing LambdaMART for cold start recommendations—0
Fast Low-Rank Bayesian Matrix Completion with Hierarchical Gaussian Prior Models—0
Collaborative Self-Attention for Recommender Systems—0
A Novel Plug-and-Play Approach for Adversarially Robust Generalization—0
A generalised log-determinant regularizer for online semi-definite programming and its applications—0
Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm—0
Collaborative Filtering and Multi-Label Classification with Matrix Factorization—0
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