Researchers have developed a novel solver for graph $p$-Laplacian semi-supervised learning that achieves near-linear time complexity. This new method addresses limitations of existing solvers, particularly at higher values of $p$ where the system can become ill-conditioned. By employing a continuation approach in $p$ and a damped chord-Newton method, the solver maintains well-conditioned systems, leading to significant speedups and reduced memory usage compared to direct factorization methods. The approach demonstrates improved performance on large-scale graph families and benchmark datasets like MNIST, outperforming standard quadratic ($p=2$) methods. AI
IMPACT This research offers a more efficient method for graph-based semi-supervised learning, potentially enabling larger and more complex datasets to be processed.
RANK_REASON The cluster contains a research paper detailing a new algorithmic approach for a specific machine learning task. [lever_c_demoted from research: ic=1 ai=1.0]
- Chord-Newton
- Dirichlet
- Ferrovie Calabro Lucane
- Graph $p$-Laplacian
- Laplace operator
- MNIST database
- Nadler-Srebro-Zhou
- semi-supervised learning
- SuiteSparse
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