An Aligned Subtree Kernel for Weighted Graphs

Lu Bai, Luca Rossi, Zhihong Zhang, Edwin Hancock
Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:30-39, 2015.

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 \textbfaligned 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v37-bai15, title = {An Aligned Subtree Kernel for Weighted Graphs}, author = {Bai, Lu and Rossi, Luca and Zhang, Zhihong and Hancock, Edwin}, booktitle = {Proceedings of the 32nd International Conference on Machine Learning}, pages = {30--39}, year = {2015}, editor = {Bach, Francis and Blei, David}, volume = {37}, series = {Proceedings of Machine Learning Research}, address = {Lille, France}, month = {07--09 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v37/bai15.pdf}, url = {https://proceedings.mlr.press/v37/bai15.html}, 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 \textbfaligned 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.} }
Endnote
%0 Conference Paper %T An Aligned Subtree Kernel for Weighted Graphs %A Lu Bai %A Luca Rossi %A Zhihong Zhang %A Edwin Hancock %B Proceedings of the 32nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2015 %E Francis Bach %E David Blei %F pmlr-v37-bai15 %I PMLR %P 30--39 %U https://proceedings.mlr.press/v37/bai15.html %V 37 %X 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 \textbfaligned 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.
RIS
TY - CPAPER TI - An Aligned Subtree Kernel for Weighted Graphs AU - Lu Bai AU - Luca Rossi AU - Zhihong Zhang AU - Edwin Hancock BT - Proceedings of the 32nd International Conference on Machine Learning DA - 2015/06/01 ED - Francis Bach ED - David Blei ID - pmlr-v37-bai15 PB - PMLR DP - Proceedings of Machine Learning Research VL - 37 SP - 30 EP - 39 L1 - http://proceedings.mlr.press/v37/bai15.pdf UR - https://proceedings.mlr.press/v37/bai15.html AB - 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 \textbfaligned 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. ER -
APA
Bai, L., Rossi, L., Zhang, Z. & Hancock, E.. (2015). An Aligned Subtree Kernel for Weighted Graphs. Proceedings of the 32nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 37:30-39 Available from https://proceedings.mlr.press/v37/bai15.html.

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