Deep Weisfeiler-Lehman assignment kernels via multiple kernel learning

Deep Weisfeiler-Lehman assignment kernels via multiple kernel learning

Abstract

Kernels for structured data are commonly obtained by decomposingobjects into their parts and adding up the similarities between all pairs of partsmeasured by a base kernel. Assignment kernels are based on an optimal bijectionbetween the parts and have proven to be an effective alternative to the establishedconvolution kernels. We explore how the base kernel can be learned as part ofthe classification problem. We build on the theory of valid assignment kernelsderived from hierarchies defined on the parts. We show that the weights of thishierarchy can be optimized via multiple kernel learning. We apply this result tolearn vertex similarities for the Weisfeiler-Lehman optimal assignment kernel forgraph classification. We present first experimental results which demonstrate thefeasibility and effectiveness of the approach.

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Authors
  • Kriege, Nils M.
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Shortfacts
Category
Paper in Conference Proceedings or in Workshop Proceedings (Paper)
Event Title
27th European Symposium on Artificial Neural Networks (ESANN)
Divisions
Data Mining and Machine Learning
Event Location
Bruges, Belgium
Event Type
Conference
Event Dates
24.-26.04.2019
Date
24 April 2019
Official URL
http://www.elen.ucl.ac.be/Proceedings/esann/esannp...
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