A transitive aligned Weisfeiler-Lehman subtree kernel

Lu Bai; Luca Rossi; Lixin Cui; Edwin R. Hancock

doi:10.1109/ICPR.2016.7899666

A transitive aligned Weisfeiler-Lehman subtree kernel

Lu Bai, Luca Rossi, Lixin Cui^*, Edwin R. Hancock

^*Corresponding author for this work

College of Engineering and Physical Sciences

Research output: Chapter in Book/Published conference output › Conference publication

86 Downloads (Pure)

Abstract

In this paper, we develop a new transitive aligned Weisfeiler-Lehman subtree kernel. This kernel not only overcomes the shortcoming of ignoring correspondence information between isomorphic substructures that arises in existing R-convolution kernels, but also guarantees the transitivity between the correspondence information that is not available for existing matching kernels. Our kernel outperforms state-of-the-art graph kernels in terms of classification accuracy on standard graph datasets.

Original language	English
Title of host publication	2016 23rd International Conference on Pattern Recognition, ICPR
Publisher	IEEE
Pages	396-401
Number of pages	6
ISBN (Electronic)	978-1-5090-4847-2
DOIs	https://doi.org/10.1109/ICPR.2016.7899666
Publication status	Published - 13 Apr 2017
Event	23rd International Conference on Pattern Recognition: ICPR 2016 - Cancun, Mexico Duration: 4 Dec 2016 → 8 Dec 2016

Conference

Conference	23rd International Conference on Pattern Recognition
Country/Territory	Mexico
City	Cancun
Period	4/12/16 → 8/12/16

Bibliographical note

-© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Access to Document

10.1109/ICPR.2016.7899666

Transitive aligned Weisfeiler-Lehman subtree Kernel
© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Accepted author manuscript, 152 KB

Cite this

@inproceedings{9fb68c789c27452d81c90e6e2e1ba7f0,

title = "A transitive aligned Weisfeiler-Lehman subtree kernel",

abstract = "In this paper, we develop a new transitive aligned Weisfeiler-Lehman subtree kernel. This kernel not only overcomes the shortcoming of ignoring correspondence information between isomorphic substructures that arises in existing R-convolution kernels, but also guarantees the transitivity between the correspondence information that is not available for existing matching kernels. Our kernel outperforms state-of-the-art graph kernels in terms of classification accuracy on standard graph datasets.",

author = "Lu Bai and Luca Rossi and Lixin Cui and Hancock, {Edwin R.}",

note = "-{\textcopyright} 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.; 23rd International Conference on Pattern Recognition : ICPR 2016 ; Conference date: 04-12-2016 Through 08-12-2016",

year = "2017",

month = apr,

day = "13",

doi = "10.1109/ICPR.2016.7899666",

language = "English",

pages = "396--401",

booktitle = "2016 23rd International Conference on Pattern Recognition, ICPR",

publisher = "IEEE",

address = "United States",

}

TY - GEN

T1 - A transitive aligned Weisfeiler-Lehman subtree kernel

AU - Bai, Lu

AU - Rossi, Luca

AU - Cui, Lixin

AU - Hancock, Edwin R.

N1 - -© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

PY - 2017/4/13

Y1 - 2017/4/13

N2 - In this paper, we develop a new transitive aligned Weisfeiler-Lehman subtree kernel. This kernel not only overcomes the shortcoming of ignoring correspondence information between isomorphic substructures that arises in existing R-convolution kernels, but also guarantees the transitivity between the correspondence information that is not available for existing matching kernels. Our kernel outperforms state-of-the-art graph kernels in terms of classification accuracy on standard graph datasets.

AB - In this paper, we develop a new transitive aligned Weisfeiler-Lehman subtree kernel. This kernel not only overcomes the shortcoming of ignoring correspondence information between isomorphic substructures that arises in existing R-convolution kernels, but also guarantees the transitivity between the correspondence information that is not available for existing matching kernels. Our kernel outperforms state-of-the-art graph kernels in terms of classification accuracy on standard graph datasets.

UR - http://www.scopus.com/inward/record.url?scp=85019065361&partnerID=8YFLogxK

U2 - 10.1109/ICPR.2016.7899666

DO - 10.1109/ICPR.2016.7899666

M3 - Conference publication

AN - SCOPUS:85019065361

SP - 396

EP - 401

BT - 2016 23rd International Conference on Pattern Recognition, ICPR

PB - IEEE

T2 - 23rd International Conference on Pattern Recognition

Y2 - 4 December 2016 through 8 December 2016

ER -

A transitive aligned Weisfeiler-Lehman subtree kernel

Abstract

Conference

Bibliographical note

Access to Document

Other files and links

Fingerprint

A novel entropy-based graph signature from the average mixing matrix

A novel text structure feature extractor for Chinese scene text detection and recognition

Cite this