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A unified framework for harmonic analysis of functions on directed graphs and changing data

机译:有向图和变化数据上的函数谐波分析的统一框架

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We present a general framework for studying harmonic analysis of functions in the settings of various emerging problems in the theory of diffusion geometry. The starting point of the now classical diffusion geometry approach is the construction of a kernel whose discretization leads to an undirected graph structure on an unstructured data set. We study the question of constructing such kernels for directed graph structures, and argue that our construction is essentially the only way to do so using discretizations of kernels. We then use our previous theory to develop harmonic analysis based on the singular value decomposition of the resulting non-self-adjoint operators associated with the directed graph. Next, we consider the question of how functions defined on one space evolve to another space in the paradigm of changing data sets recently introduced by Coifman and Hirn. While the approach of Coifman and Hirn requires that the points on one space should be in a known one-to-one correspondence with the points on the other, our approach allows the identification of only a subset of landmark points. We introduce a new definition of distance between points on two spaces, construct localized kernels based on the two spaces and certain interaction parameters, and study the evolution of smoothness of a function on one space to its lifting to the other space via the landmarks. We develop novel mathematical tools that enable us to study these seemingly different problems in a unified manner. (c) 2016 Elsevier Inc. All rights reserved.
机译:我们提出了一个通用框架,用于研究扩散几何学理论中各种新出现的问题中函数的谐波分析。现在经典的扩散几何方法的出发点是构建一个核,该核的离散化导致非结构化数据集上的无向图结构。我们研究了为有向图结构构造这样的内核的问题,并认为我们的构造本质上是使用内核离散化的唯一方法。然后,我们使用先前的理论,根据与有向图相关的结果非自伴算子的奇异值分解,开发谐波分析。接下来,我们考虑一个问题,即在Coifman和Hirn最近提出的更改数据集的范式中,在一个空间上定义的功能如何演变为另一个空间。尽管Coifman和Hirn的方法要求一个空间上的点应与另一空间上的点一一对应,但我们的方法仅允许识别界标点的子集。我们引入了两个空间上的点之间距离的新定义,基于两个空间和某些交互参数构造局部核,并研究了一个功能在一个空间上的平滑度从地标提升到另一个空间的平滑度的演变。我们开发了新颖的数学工具,使我们能够以统一的方式研究这些看似不同的问题。 (c)2016 Elsevier Inc.保留所有权利。

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