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Algebraic Multigrid Acceleration for Efficient Label Spreading — arXiv:2608.26309

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.26309
License (as archived): CC0-1.0
Archived: 2026-10-17T16:09:19.546Z
SHA-256 of the archived text: 36673db604ae58c3cb5e743b3caac5642018f948ee8ff928c33ae522f1965ee0
Stable URL of this page: https://metraipolis.com/documento/dc_deb7903ec0ed

How to cite

Algebraic Multigrid Acceleration for Efficient Label Spreading — arXiv:2608.26309 — arXiv (en), https://arxiv.org/abs/2608.26309 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_deb7903ec0ed (Metraipolis archive dc_deb7903ec0ed, sha256 36673db604ae58c3cb5e743b3caac5642018f948ee8ff928c33ae522f1965ee0)

BibTeX

@misc{dcdeb7903ec0ed,
  title = {Algebraic Multigrid Acceleration for Efficient Label Spreading — arXiv:2608.26309},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.26309},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_deb7903ec0ed; sha256 36673db604ae58c3cb5e743b3caac5642018f948ee8ff928c33ae522f1965ee0},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.26309. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Modern machine learning models rely on large amounts of labeled data. However, manual annotation of large-scale datasets is expensive and time-consuming. Label spreading is a semi-supervised learning technique that addresses this challenge by propagating information from a few labeled examples to a larger pool of unlabeled data. Despite its effectiveness, its application to large-scale, high-dimensional datasets is limited by computational costs and memory constraints. To address these limitations, we propose Algebraic Multigrid Acceleration for Efficient Label Spreading (AMELS), an efficient label spreading framework that improves scalability by fast construction of neighborhood graphs and the incorporation of algebraic multigrid solvers. The latter is an iterative solver that replaces the ordinary random walk iteration typically performed in label spreading. Due to the multilevel nature of algebraic multigrid solvers, AMELS spreads given label information across a graph of any size in a single multigrid cycle. We demonstrate that AMELS achieves significant runtime reductions compared to existing implementations while also being more robust to hyperparameter choices in terms of both runtime and classification accuracy. Our framework therefore enables efficient label spreading on large-scale image datasets and produces accurate labels even when only a few labeled samples are available.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-28 in cs.LG).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2608.26309 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T15:34:49.889Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-08-28?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).