Information Theoretic Prototype Selection for Unattributed Graphs

Han, L.; Rossi, Luca; Torsello, Andrea; Wilson, Richard C.; Hancock, Edwin R.

doi:10.1007/978-3-642-34166-3_4

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…(2) is unsuitable because the unmatched vertices and edges in G ′ are not accounted for in Eq. (2). Therefore, we impose penalty η on unmatched vertices and edges.…”

Section: Similaritymentioning

confidence: 99%

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Structural Data Recognition With Graph Model Boosting

Miyazaki

Omachi

2018

IEEE Access

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This paper presents a novel method for structural data recognition using a large number of graph models. In general, prevalent methods for structural data recognition have two shortcomings: 1) Only a single model is used to capture structural variation. 2) Naive recognition methods are used, such as the nearest neighbor method. In this paper, we propose strengthening the recognition performance of these models as well as their ability to capture structural variation. The proposed method constructs a large number of graph models and trains decision trees using the models. This paper makes two main contributions. The first is a novel graph model that can quickly perform calculations, which allows us to construct several models in a feasible amount of time. The second contribution is a novel approach to structural data recognition: graph model boosting. Comprehensive structural variations can be captured with a large number of graph models constructed in a boosting framework, and a sophisticated classifier can be formed by aggregating the decision trees. Consequently, we can carry out structural data recognition with powerful recognition capability in the face of comprehensive structural variation. The experiments shows that the proposed method achieves impressive results and outperforms existing methods on datasets of IAM graph database repository. ✦ • T. Miyazaki and S. Omachi are with the

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“…(2) is unsuitable because the unmatched vertices and edges in G ′ are not accounted for in Eq. (2). Therefore, we impose penalty η on unmatched vertices and edges.…”

Section: Similaritymentioning

confidence: 99%

“…Even measuring the distance between graphs requires various techniques. The problem of graph recognition has recently been actively studied [1], [2], [3], [4], [5]. Past research has led to notable progress in two aspects.…”

Section: Introductionmentioning

confidence: 99%