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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 41–50 of 796 papers

TitleStatusHype
A Comparison of Clustering and Missing Data Methods for Health Sciences—0
Abrupt Learning in Transformers: A Case Study on Matrix Completion—0
A Characterization of Deterministic Sampling Patterns for Low-Rank Matrix Completion—0
Low-rank matrix completion theory via Plucker coordinates—0
Advancing Thermodynamic Group-Contribution Methods by Machine Learning: UNIFAC 2.0—0
A More Stable Accelerated Gradient Method Inspired by Continuous-Time Perspective—0
A Max-Norm Constrained Minimization Approach to 1-Bit Matrix Completion—0
Advancing Matrix Completion by Modeling Extra Structures beyond Low-Rankness—0
Amplify Graph Learning for Recommendation via Sparsity Completion—0
A Block Lanczos with Warm Start Technique for Accelerating Nuclear Norm Minimization Algorithms—0
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