An aligned subtree kernel for weighted graphs

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

Research output: Chapter in Book/Published conference outputConference publication


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
ISSN (Electronic)1533-7928


Conference32nd International Conference on Machine Learning
Abbreviated titleICML Lille

Bibliographical note

© The authors


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