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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 51–60 of 796 papers

TitleStatusHype
Leave-One-Out Analysis for Nonconvex Robust Matrix Completion with General Thresholding Functions—0
Advancing Thermodynamic Group-Contribution Methods by Machine Learning: UNIFAC 2.0—0
Predictive Low Rank Matrix Learning under Partial Observations: Mixed-Projection ADMMCode0
Generalized Low-Rank Matrix Completion Model with Overlapping Group Error Representation—0
The heterogeneous impact of the EU-Canada agreement with causal machine learning—0
Optimized Waveform Design for OFDM-based ISAC Systems Under Limited Resource Occupancy—0
Amplify Graph Learning for Recommendation via Sparsity Completion—0
Proximal Interacting Particle Langevin AlgorithmsCode0
Demystifying Language Model Forgetting with Low-rank Example Associations—0
Learning Translations via Matrix Completion—0
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