English(EN) A General Kernel Framework for Non-CND Distance Measures Using |D|-Dimensional Sparse Landmark Embeddings

新的 SLE 核框架绕过了高斯过程的距离度量要求

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-17 04:00

研究人员引入了一个名为稀疏地标嵌入（SLE）核的新核框架，旨在克服现有核方法（如高斯过程，GPs）的局限性。与要求特定距离度量以确保半正定性的传统方法不同，SLE 核可与任何距离度量配合使用。它通过将输入嵌入稀疏特征向量来实现这一点，从而保持了计算的可行性并对其性能提供了理论保证。 AI

影响这个新的核框架可以使高斯过程和其他核方法更广泛地应用于各种数据集和距离度量。

排序理由该集群包含一篇详细介绍机器学习新核框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv stat.ML TIER_1 English(EN) · Marcus M. Noack, Maher B. Alghalayini, Mark D. Risser · 2026-09-17 04:00

基于 |D| 维稀疏地标嵌入的非 CND 距离度量的通用核框架

arXiv:2609.19083v1 Announce Type: new Abstract: Kernel methods, and Gaussian Processes (GPs) in particular, require a Hilbertian distance measure---one whose square is conditionally negative definite (CND)---to guarantee positive semi-definiteness (PSD) of the kernel matrix; a co…