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新的随机投影分析优化了 Sobolev 范数下的谱算法

本文改进了基于随机投影的谱算法的分析,重点关注其在一般条件下的泛化性能。作者利用再生核希尔伯特空间的嵌入性质,在 Sobolev 范数下建立了最优学习率,与 minimax 下界相匹配。他们的研究结果表明,最优性所需的投影维度与有效维度成正比,在保持各种来源条件下的统计效率的同时,显著降低了计算成本。实验结果验证了所提出方法的理论发现和实际有效性。 AI

影响 优化了谱算法的计算成本,可能在机器学习应用中实现更高效的数据分析。

排序理由 该条目是一篇学术论文,详细介绍了机器学习算法的理论进展。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新的随机投影分析优化了 Sobolev 范数下的谱算法

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该条目是一篇学术论文,详细介绍了机器学习算法的理论进展。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Caixing Wang, Yue Wang ·

    Random Projections for Spectral Algorithms: Optimal Rates in Sobolev Norms

    arXiv:2610.30948v1 Announce Type: cross Abstract: Random projections (RP) offer an effective method for reducing computational and storage overhead while preserving the geometric structure of data. However, existing studies have primarily focused on the optimal generalization per…