Iterative Bipartite Graph Edit Distance Approximation

Kaspar Riesen, Rolf Dornberger, H. Bunke
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引用次数: 5

Abstract

One of the major tasks in many applications in the field of document analysis is the computation of dissimilarities between two or more objects from a given problem domain. Hence, employing graphs as representation formalism evokes the need for powerful, fast and flexible graph based dissimilarity models. Graph edit distance is powerful and applicable to any kind of graphs but suffers from its high computational complexity. Recently, however, a novel framework for graph edit distance approximation has been introduced. While the run time of this novel procedure is very convincing, the precision of the approximated graph distances is dissatisfying in some cases. The present paper introduces a generalized version of the existing approximation framework using an iterative bipartite procedure. With empirical investigations on three real world data sets we show that our extension substantially improves the accuracy of the approximations while the run time is increased only linearly with the number of additional iterations.
迭代二部图编辑距离逼近
在文档分析领域的许多应用程序中,主要任务之一是计算给定问题域中两个或多个对象之间的不相似性。因此,使用图作为表示形式唤起了对强大、快速和灵活的基于图的不相似模型的需求。图形编辑距离功能强大,适用于任何类型的图形,但其计算复杂度较高。然而,最近提出了一种新的图形编辑距离近似框架。虽然这种新方法的运行时间非常令人信服,但在某些情况下,近似图距离的精度令人不满意。本文采用迭代二部过程,介绍了现有近似框架的一个推广版本。通过对三个真实世界数据集的实证研究,我们表明我们的扩展极大地提高了近似的准确性,而运行时间仅随着额外迭代次数的增加而线性增加。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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