Torsello, Andrea, Gasparetto, Andrea, Rossi, Luca et al. (2 more authors) (2014) Transitive state alignment for the quantum jensen-shannon kernel. In: Fränti,, Pasi, Brown, Gavin, Loog, Marco, Escolano, Francisco and Pelillo, Marcello, (eds.) Structural, Syntactic, and Statistical Pattern Recognition:Joint IAPR International Workshop, S+SSPR 2014, Joensuu, Finland, August 20-22, 2014. Proceedings. Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, S+SSPR 2014, 20-22 Aug 2014 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) . Springer , GBR , pp. 22-31.
Abstract
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.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © Springer Verlag 2014. This is an author produced version of a paper accepted for publication in Lecture Notes in Computer Science. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) The University of York |
Depositing User: | Pure (York) |
Date Deposited: | 15 Dec 2015 13:52 |
Last Modified: | 16 Oct 2024 10:43 |
Published Version: | https://doi.org/10.1007/978-3-662-44415-3_3 |
Status: | Published |
Publisher: | Springer |
Series Name: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Identification Number: | 10.1007/978-3-662-44415-3_3 |
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Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:85368 |
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