scispace - formally typeset
Book ChapterDOI

A quadratic programming approach to the graph edit distance problem

Michel Neuhaus, +1 more
- pp 92-102
Reads0
Chats0
TLDR
Experiments demonstrate that the proposed quadratic programming approach to computing the edit distance of graphs is able to outperform the standard edit distance method in terms of recognition accuracy on two out of three data sets.
Abstract: 
In this paper we propose a quadratic programming approach to computing the edit distance of graphs. Whereas the standard edit distance is defined with respect to a minimum-cost edit path between graphs, we introduce the notion of fuzzy edit paths between graphs and provide a quadratic programming formulation for the minimization of fuzzy edit costs. Experiments on real-world graph data demonstrate that our proposed method is able to outperform the standard edit distance method in terms of recognition accuracy on two out of three data sets.

read more

Content maybe subject to copyright    Report

Citations
More filters
Journal ArticleDOI

A review of multi-instance learning assumptions

James R. Foulds, +1 more
- 01 Mar 2010 - 
TL;DR: This paper aims to clarify the use of alternative MI assumptions by reviewing the work done in this area, and focuses on a relaxed view of the MI problem, where the standard MI assumption is dropped and alternative assumptions are considered instead.
Posted Content

Multi-Instance Learning by Treating Instances As Non-I.I.D. Samples

Zhi-Hua Zhou, +2 more
- 12 Jul 2008 - 
TL;DR: This paper explicitly map every bag to an undirected graph and design a graph kernel for distinguishing the positive and negative bags and implicitly construct graphs by deriving affinity matrices and propose an efficient graph kernel considering the clique information.
Proceedings ArticleDOI

Multi-instance learning by treating instances as non-I.I.D. samples

Zhi-Hua Zhou, +2 more
TL;DR: In this article, the instances in a bag are rarely independent in real tasks, and a better performance can be expected if the instances are treated in an non-i.i.d. way that exploits relations among instances.
Proceedings ArticleDOI

A Short Survey of Recent Advances in Graph Matching

Junchi Yan, +5 more
TL;DR: The aim is to provide a systematic and compact framework regarding the recent development and the current state-of-the-arts in graph matching.
Journal ArticleDOI

The graph matching problem

Lorenzo Livi, +1 more
- 01 Aug 2013 - 
TL;DR: This paper considers different classes of graphs that are roughly differentiated considering the complexity of the defined labels for both vertices and edges, aiming at explaining some significant instances of each graph matching methodology mainly considered in the technical literature.
References
More filters
Book

Numerical Optimization

Jorge Nocedal, +1 more
TL;DR: Numerical Optimization presents a comprehensive and up-to-date description of the most effective methods in continuous optimization, responding to the growing interest in optimization in engineering, science, and business by focusing on the methods that are best suited to practical problems.
Book

Learning with kernels

Bernhard Schölkopf
Journal ArticleDOI

The String-to-String Correction Problem

Robert A. Wagner, +1 more
- 01 Jan 1974 - 
TL;DR: An algorithm is presented which solves the string-to-string correction problem in time proportional to the product of the lengths of the two strings.
Journal ArticleDOI

A distance measure between attributed relational graphs for pattern recognition

Alberto Sanfeliu, +1 more
TL;DR: A method to determine a distance measure between two nonhierarchical attributed relational graphs is presented and an application of this distance measure to the recognition of lower case handwritten English characters is presented.
Journal ArticleDOI

Structural matching in computer vision using probabilistic relaxation

William J. Christmas, +2 more
- 01 Aug 1995 - 
TL;DR: The theory of probabilistic relaxation for matching features extracted from 2D images is developed, derive as limiting cases the various heuristic formulae used by researchers in matching problems, and state the conditions under which they apply.