An aligned subtree kernel for weighted graphs

Lu Bai, Luca Rossi, Zhihong Zhang, Edwin R. Hancock

Research output: Chapter in Book/Report/Conference proceedingConference publication

Abstract

In this paper, we develop a new entropic matching kernel for weighted graphs by aligning depth-based representations. We demonstrate that this kernel can be seen as an aligned subtree kernel that incorporates explicit subtree correspondences, and thus addresses the drawback of neglecting the relative locations between substructures that arises in the R-convolution kernels. Experiments on standard datasets demonstrate that our kernel can easily outperform state-of-the-art graph kernels in terms of classification accuracy.
Original languageEnglish
Title of host publicationProceedings of the 32nd International Conference on Machine Learning
EditorsFrancis Bach, David Blei
Number of pages10
Publication statusPublished - 2015
Event32nd International Conference on Machine Learning - Lille Grand Palais, Lille, France
Duration: 6 Jul 201511 Jul 2015

Publication series

Name JMLR workshop and conference proceedings
PublisherJMLR
Volume37
ISSN (Electronic)1533-7928

Conference

Conference32nd International Conference on Machine Learning
Abbreviated titleICML Lille
CountryFrance
CityLille
Period6/07/1511/07/15

Bibliographical note

© The authors

Fingerprint Dive into the research topics of 'An aligned subtree kernel for weighted graphs'. Together they form a unique fingerprint.

  • Cite this

    Bai, L., Rossi, L., Zhang, Z., & Hancock, E. R. (2015). An aligned subtree kernel for weighted graphs. In F. Bach, & D. Blei (Eds.), Proceedings of the 32nd International Conference on Machine Learning ( JMLR workshop and conference proceedings; Vol. 37). http://jmlr.org/proceedings/papers/v37/bai15.pdf