超图 p-Laplacians 和尺度空间

IF 1.3 4区 数学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Ariane Fazeny, Daniel Tenbrinck, Kseniia Lukin, Martin Burger
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引用次数: 0

摘要

本文旨在重温超图上微分算子的定义,超图是基于成对之外的相互作用的系统中图的自然扩展。我们特别关注有向和无向超图的拉普拉斯算子和 p-Laplace 算子的定义、基本性质、变异结构及其尺度空间。我们说明,超图上的扩散方程是社交网络信息流或图像处理等不同应用的可能模型。此外,这些算子引起的谱分析和尺度空间为进一步分析复杂数据及其多尺度结构提供了一种潜在方法。在超图上寻求频谱分析和合适的尺度空间,尤其需要定义具有微不足道的第一特征函数的微分算子,从而获得更多可解释的第二特征函数。现有的超图 p-Laplacians 定义并不能自动满足这一属性,因此我们提供了一种新的公理方法,它扩展了以前的定义,并可专门用于满足此类(或其他)所需属性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Hypergraph p-Laplacians and Scale Spaces

Hypergraph p-Laplacians and Scale Spaces

The aim of this paper is to revisit the definition of differential operators on hypergraphs, which are a natural extension of graphs in systems based on interactions beyond pairs. In particular, we focus on the definition of Laplacian and p-Laplace operators for oriented and unoriented hypergraphs, their basic properties, variational structure, and their scale spaces. We illustrate that diffusion equations on hypergraphs are possible models for different applications such as information flow on social networks or image processing. Moreover, the spectral analysis and scale spaces induced by these operators provide a potential method to further analyze complex data and their multiscale structure. The quest for spectral analysis and suitable scale spaces on hypergraphs motivates in particular a definition of differential operators with trivial first eigenfunction and thus more interpretable second eigenfunctions. This property is not automatically satisfied in existing definitions of hypergraph p-Laplacians, and we hence provide a novel axiomatic approach that extends previous definitions and can be specialized to satisfy such (or other) desired properties.

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来源期刊
Journal of Mathematical Imaging and Vision
Journal of Mathematical Imaging and Vision 工程技术-计算机:人工智能
CiteScore
4.30
自引率
5.00%
发文量
70
审稿时长
3.3 months
期刊介绍: The Journal of Mathematical Imaging and Vision is a technical journal publishing important new developments in mathematical imaging. The journal publishes research articles, invited papers, and expository articles. Current developments in new image processing hardware, the advent of multisensor data fusion, and rapid advances in vision research have led to an explosive growth in the interdisciplinary field of imaging science. This growth has resulted in the development of highly sophisticated mathematical models and theories. The journal emphasizes the role of mathematics as a rigorous basis for imaging science. This provides a sound alternative to present journals in this area. Contributions are judged on the basis of mathematical content. Articles may be physically speculative but need to be mathematically sound. Emphasis is placed on innovative or established mathematical techniques applied to vision and imaging problems in a novel way, as well as new developments and problems in mathematics arising from these applications. The scope of the journal includes: computational models of vision; imaging algebra and mathematical morphology mathematical methods in reconstruction, compactification, and coding filter theory probabilistic, statistical, geometric, topological, and fractal techniques and models in imaging science inverse optics wave theory. Specific application areas of interest include, but are not limited to: all aspects of image formation and representation medical, biological, industrial, geophysical, astronomical and military imaging image analysis and image understanding parallel and distributed computing computer vision architecture design.
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