Green Version.pdf

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Preview of Green Version
🔗 Source: eprints.whiterose.ac.uk
📊 Size: 3.99 MB
👤 Author: Rossi, Luca, Torsello, Andrea and Hancock, Edwin R
⬇️ Downloads: 123

Summary

Researchers investigated using manifold learning to enhance class separation in graph kernels, observing that multidimensional scaling of kernel distance matrices results in data clustered along a curved embedding space. They proposed using manifold learning to compute a low-dimensional graph embedding, unfolding the embedding manifold to increase class separation. Experiments on standard graph datasets using shortest-path, graphlet, random walk, and Weisfeiler-Lehman kernels showed significant improvement with the graphlet kernel, which neglects locational information of substructures, leading to stronger curvature of the embedding manifold. The unfolding reduced the performance gap between the examined kernels.

Description

Researchers investigated using manifold learning to enhance class separation in graph kernels, observing that multidimensional scaling of kernel distance...

Technical Information

  • File Format: PDF
  • File Size: 3.99 MB
  • Pages: 34
  • Language: EN
  • Author: Rossi, Luca, Torsello, Andrea and Hancock, Edwin R
  • Total Downloads: 123
  • Last Updated: 6 days ago

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