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Manifold Learning and the Quantum Jensen-Shannon Divergence Kernel

  • Luca Rossi
  • Andrea Torsello
  • Edwin R. Hancock
Conference paper
  • 2.2k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8047)

Abstract

The quantum Jensen-Shannon divergence kernel [1] was recently introduced in the context of unattributed graphs where it was shown to outperform several commonly used alternatives. In this paper, we study the separability properties of this kernel and we propose a way to compute a low-dimensional kernel embedding where the separation of the different classes is enhanced. The idea stems from the observation that the multidimensional scaling embeddings on this kernel show a strong horseshoe shape distribution, a pattern which is known to arise when long range distances are not estimated accurately. Here we propose to use Isomap to embed the graphs using only local distance information onto a new vectorial space with a higher class separability. The experimental evaluation shows the effectiveness of the proposed approach.

Keywords

Graph Kernels Manifold Learning Continuous-Time Quantum Walk Quantum Jensen-Shannon Divergence 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Luca Rossi
    • 1
  • Andrea Torsello
    • 1
  • Edwin R. Hancock
    • 2
  1. 1.Department of Environmental Science, Informatics and StatisticsCa’ Foscari University of VeniceItaly
  2. 2.Department of Computer ScienceUniversity of YorkUK

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