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An eigenspace projection clustering method for inexact graph matching.

Terry Caelli1, Serhiy Kosinov

  • 1Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada, T6G 2H1. tcaelli@ualberta.ca

IEEE Transactions on Pattern Analysis and Machine Intelligence
|September 24, 2004
PubMed
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This study introduces eigenspace renormalization projection clustering (EPC), a novel method for inexact graph matching. EPC effectively matches graphs of varying sizes using vertex projections and relational clustering.

Area of Science:

  • Computational mathematics
  • Graph theory
  • Machine learning

Background:

  • Graph matching is crucial for comparing complex structures.
  • Existing methods struggle with graphs of different sizes.
  • Vertex connectivity is a key feature for graph comparison.

Purpose of the Study:

  • To develop a robust method for inexact graph matching.
  • To enable matching of graphs with differing numbers of vertices.
  • To introduce the eigenspace renormalization projection clustering (EPC) method.

Main Methods:

  • Utilizing vertex projections into a joint eigenspace.
  • Applying relational clustering techniques.
  • Employing eigenspace renormalization for feature extraction.

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Main Results:

  • Demonstrated successful inexact graph matching.
  • EPC effectively handles graphs with different vertex counts.
  • Validation using shock graph-based shape matching and random graphs yielded encouraging results.

Conclusions:

  • Eigenspace renormalization projection clustering (EPC) offers a powerful solution for inexact graph matching.
  • The method's adaptability to graphs of varying sizes is a significant advancement.
  • Further exploration with random graphs confirms the approach's objectivity and effectiveness.