The content discusses sparsification of the regularized magnetic Laplacian using multi-type spanning forests. It explores applications in angular synchronization and semi-supervised learning, providing statistical guarantees and practical implications. The paper introduces a novel approach, sparsify-and-eigensolve, for approximating eigenvectors and sparsify-and-precondition for improving numerical convergence. Sampling methods like CyclePopping are highlighted for fast sampling of MTSFs. Empirical results on ranking and preconditioning systems are presented, along with limitations and notations used.
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by Mich... às arxiv.org 03-21-2024
https://arxiv.org/pdf/2208.14797.pdfPerguntas Mais Profundas