English(EN) Low-Dimensional Embeddings for Gaussian Kernels on Manifolds

新方法为流形上的高斯核提供了高效的低维嵌入

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

研究人员开发了一种新的方法，用于在流形上创建高斯核的低维嵌入。该技术改进了Chen和Phillips之前的工作，为计算任意子流形上点的成对高斯核距离提供了一种更有效的方法。新的嵌入在指定的误差范围内保留了成对距离，并保持了拓扑信息，确保了持久同调的保留。 AI

影响这项研究可能导致更多依赖核方法进行数据分析和机器学习的AI模型更加高效。

排序理由该集群包含一篇研究论文，详细介绍了一种用于低维嵌入的新数学方法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Soumik Dutta, Kunal Dutta · 2026-09-15 04:00

流形上高斯核的低维嵌入

arXiv:2609.15179v1 Announce Type: cross Abstract: The Gaussian kernel is a widely used similarity measure underlying kernel methods such as kernel PCA and spectral clustering, but computing Gaussian kernel distances for many pairs of points can be expensive. Using Random Fourier …