A nested alignment graph kernel through the dynamic time warping framework

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

*Corresponding author for this work

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

Abstract

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.

Original languageEnglish
Title of host publicationGraph-based representations in pattern recognition : 11th IAPR-TC-15 international workshop, GbRPR 2017. Proceedings
EditorsPasquale Foggia, Cheng-Lin Liu, Mario Vento
Place of PublicationCham (CH)
PublisherSpringer
Pages59-69
Number of pages11
ISBN (Electronic)978-3-319-58961-9
ISBN (Print)978-3-319-58960-2
DOIs
Publication statusPublished - 2017
Event11th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2017 - Anacapri, Italy
Duration: 16 May 201718 May 2017

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume10310
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Workshop

Workshop11th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2017
CountryItaly
CityAnacapri
Period16/05/1718/05/17

Fingerprint Dive into the research topics of 'A nested alignment graph kernel through the dynamic time warping framework'. Together they form a unique fingerprint.

  • Research Output

    • 2 Conference contribution

    Adaptive feature selection based on the most informative graph-based features

    Bai, L., Cui, L., Rossi, L., Hancock, E. R. & Jiao, Y., 2017, Graph-based representations in pattern recognition : 11th IAPR-TC-15 international workshop, GbRPR 2017. Proceedings. Foggia, P., Liu, C-L. & Vento, M. (eds.). Cham (CH): Springer, p. 276-287 12 p. (Lecture Notes in Computer Science; vol. 10310).

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

    Open Access
    File
  • Measuring vertex centrality using the Holevo quantity

    Rossi, L. & Torsello, A., 2017, Graph-based representations in pattern recognition : 11th IAPR-TC-15 international workshop, GbRPR 2017. Proceedings. Foggia, P., Liu, C-L. & Vento, M. (eds.). Cham (CH): Springer, p. 154-164 11 p. (Lecture Notes in Computer Science; vol. 10310).

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

    Open Access
    File
  • Cite this

    Bai, L., Rossi, L., Cui, L., & Hancock, E. R. (2017). A nested alignment graph kernel through the dynamic time warping framework. In P. Foggia, C-L. Liu, & M. Vento (Eds.), Graph-based representations in pattern recognition : 11th IAPR-TC-15 international workshop, GbRPR 2017. Proceedings (pp. 59-69). (Lecture Notes in Computer Science; Vol. 10310). Springer. https://doi.org/10.1007/978-3-319-58961-9_6