A nested alignment graph kernel through the dynamic time warping framework


In this paper, we propose a novel nested alignment graph kernel drawing on depth-based complexity traces and the dynamic time warping framework. Specifically, for a pair of graphs, we commence by computing the depth-based complexity traces rooted at the centroid vertices. The resulting kernel for the graphs is defined by measuring the global alignment kernel, which is developed through the dynamic time warping framework, between the complexity traces. We show that the proposed kernel simultaneously considers the local and global graph characteristics in terms of the complexity traces, but also provides richer statistic measures by incorporating the whole spectrum of alignment costs between these traces. Our experiments demonstrate the effectiveness and efficiency of the proposed kernel.

Publication DOI: https://doi.org/10.1007/978-3-319-58961-9_6
Divisions: College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Computer Science
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Event Title: 11th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2017
Event Type: Other
Event Dates: 2017-05-16 - 2017-05-18
Uncontrolled Keywords: Theoretical Computer Science,Computer Science(all)
ISBN: 978-3-319-58960-2, 978-3-319-58961-9
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Conference contribution
Published Date: 2017
Published Online Date: 2017-05-10
Accepted Date: 2017-03-06
Authors: Bai, Lu
Rossi, Luca (ORCID Profile 0000-0002-6116-9761)
Cui, Lixin
Hancock, Edwin R.



Version: Accepted Version

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