Related papers: Graph-Regularized Low-Rank Matrix Completion by Variable Projection

Graph-Regularized Low-Rank Matrix Completion by Variable Projection

URL: http://arxiv.org/abs/2607.09546v1
Date: Fri, 10 Jul 2026 15:57:02 GMT
Title: Graph-Regularized Low-Rank Matrix Completion by Variable Projection
Abstract summary: Graph-Regularized RTRMC (GR-RTRMC) exploits the inherent relationships between rows and columns of the matrix.<n>We aim to improve the accuracy and robustness of matrix completion, particularly in scenarios where the underlying data exhibits strong correlations between rows or columns.
Score: 0.0
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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 exhibits strong correlations between rows or columns.

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