Transitive state alignment for the quantum jensen-shannon kernel

Andrea Torsello, Andrea Gasparetto, Luca Rossi, Lu Bai, Edwin R. Hancock

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

6 Citations (Scopus)


Kernel methods provide a convenient way to apply a wide range of learning techniques to complex and structured data by shifting the representational problem from one of finding an embedding of the data to that of defining a positive semidefinite kernel. One problem with the most widely used kernels is that they neglect the locational information within the structures, resulting in less discrimination. Correspondence-based kernels, on the other hand, are in general more discriminating, at the cost of sacrificing positive-definiteness due to their inability to guarantee transitivity of the correspondences between multiple graphs. In this paper we generalize a recent structural kernel based on the Jensen-Shannon divergence between quantum walks over the structures by introducing a novel alignment step which rather than permuting the nodes of the structures, aligns the quantum states of their walks. This results in a novel kernel that maintains localization within the structures, but still guarantees positive definiteness. Experimental evaluation validates the effectiveness of the kernel for several structural classification tasks.

Original languageEnglish
Title of host publicationStructural, Syntactic, and Statistical Pattern Recognition - Joint IAPR International Workshop, S+SSPR 2014, Proceedings
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783662444146
Publication statusPublished - Aug 2014
Externally publishedYes
EventJoint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, S+SSPR 2014 - Joensuu, Finland
Duration: 20 Aug 201422 Aug 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8621 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceJoint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, S+SSPR 2014

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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