Graph-Regularized Low-Rank Matrix Completion by Variable Projection
We address the low-rank matrix completion problem by incorporating graph regularization into the existing Riemannian Trust-Region Matrix Completion (RTRMC) framework. The latter uses the geometry of the low-rank constraint to remodel the problem as an unconstrained optimization problem on a single Grassmann manifold. Our approach, named Graph-Regularized RTRMC (GR-RTRMC), exploits the inherent relationships between rows and columns of the matrix. By using these relationships, we aim to improve the accuracy and robustness of matrix completion, particularly in scenarios where the underlying data
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- PossiblePossibly related (embedding) · 48%pyRiemann/pyRiemann →
- LinkedLinked via arxiv author · 85%Benoît Loucheur →
“Graph-Regularized Low-Rank Matrix Completion by Variable Projection”
- LinkedLinked via arxiv author · 85%P. -A. Absil →
“Graph-Regularized Low-Rank Matrix Completion by Variable Projection”
- LinkedLinked via arxiv author · 85%Michel Journée →
“Graph-Regularized Low-Rank Matrix Completion by Variable Projection”
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