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动力系统的扩散映射,谱聚类和反应坐标

Diffusion Maps, Spectral Clustering and Reaction Coordinates of Dynamical Systems
课程网址: http://videolectures.net/mlss05us_nadler_dmscr/  
主讲教师: Boaz Nadler
开课单位: 魏茨曼科学研究所
开课时间: 2007-02-25
课程语种: 英语
中文简介:
数据分析的一个核心问题是高维数据的低维表示,以及其基础几何和密度的简明描述。在分析复杂动力系统的大规模模拟时,时间演化的概念发挥作用,重要的问题是慢变量的识别和参数化它们的反应坐标的表示。在本文中,我们通过考虑一系列扩散图提供了这些明显不同任务的统一视图,扩散图定义为通过在给定数据集上定义的适当归一化随机游走的特征向量将复杂数据嵌入到低维欧几里德空间中。我们在理论上和实例中都展示了这种嵌入如何用于降维,流形学习,复杂数据集的几何分析以及随机动力系统的快速模拟。与R.R. Coifman,S. Lafon,M。Maggioni和I.G.联合工作。 Kevrekidis
课程简介: A central problem in data analysis is the low dimensional representation of high dimensional data, and the concise description of its underlying geometry and density. In the analysis of large scale simulations of complex dynamical systems, where the notion of time evolution comes into play, important problems are the identification of the slow variables and the representation of the reaction coordinates that parameterize them. In this paper we provide a unifying view of these apparently different tasks, by considering a family of diffusion maps, defined as the embedding of complex data onto a low dimensional Euclidian space, via the eigenvectors of suitably normalized random walks defined on the given datasets. We show, both theoretically and by examples how this embedding can be used for dimensionality reduction, manifold learning, geometric analysis of complex data sets and fast simulations of stochastic dynamical systems. Joint work with R.R. Coifman, S. Lafon, M. Maggioni and I.G. Kevrekidis
关 键 词: 数据分析; 高维数据; 复杂动力系统
课程来源: 视频讲座网
最后编审: 2019-07-10:lxf
阅读次数: 129