English(EN) Gaussian mixture models in Hilbert spaces via kernel methods

研究人员提出使用核方法在希尔伯特空间中实现高斯混合模型

作者 PulseAugur 编辑部 · [2 个来源] · 2026-05-07 10:48

研究人员开发了一个新的高斯混合模型框架，专为复杂、无限维度的数据（如动态函数数据）设计。该方法利用核均值嵌入，并提供了高效的估计算法，在无限维度空间中具有明确性和近似能力的理论保证。该框架在包括函数数据和医学应用中的随机图在内的各种数据类型上进行了评估。 AI

影响引入了一个处理高维和函数数据的新型统计框架，有可能改善利用此类复杂数据集的领域的聚类和分析。

排序理由这是一篇详细介绍用于建模复杂数据的新统计框架的研究论文。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

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arXiv cs.LG TIER_1 English(EN) · Daniel L\'opez-Montero, Antonio \'Alvarez-L\'opez, Marcos Matabuena · 2026-05-08 04:00

基于核方法的希尔伯特空间高斯混合模型

arXiv:2605.05996v1 Announce Type: cross Abstract: Modern datasets across many disciplines increasingly consist of time-evolving, potentially infinite-dimensional random objects, such as dynamic functional data, which are naturally modeled in Hilbert spaces. In these settings, cha…
arXiv stat.ML TIER_1 English(EN) · Marcos Matabuena · 2026-05-07 10:48

基于核方法的希尔伯特空间高斯混合模型

Modern datasets across many disciplines increasingly consist of time-evolving, potentially infinite-dimensional random objects, such as dynamic functional data, which are naturally modeled in Hilbert spaces. In these settings, characterizing probability measures, for example, thr…