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Large Sample Statistics in the Domain of Graphs

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Part of the Lecture Notes in Computer Science book series (LNIP,volume 6218)


One challenge in bridging the gap between structural and statistical pattern recognition consists in studying combinatorial structures like graphs using probabilistic methods. This contribution presents the structural counterparts of the first and second fundamental theorem in probability, (1) the law of large numbers and (2) the central limit theorem. In addition, we derive characterizations and uniqueness conditions for the mean of graphs. As a special case, we investigate the weighted mean of two graphs. The proposed results establish a sound statistical foundation for unsupervised structural pattern recognition methods.


  • Central Limit Theorem
  • Central Cluster
  • Fundamental Domain
  • Vector Representation
  • Graph Match

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Jain, B.J., Obermayer, K. (2010). Large Sample Statistics in the Domain of Graphs. In: Hancock, E.R., Wilson, R.C., Windeatt, T., Ulusoy, I., Escolano, F. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2010. Lecture Notes in Computer Science, vol 6218. Springer, Berlin, Heidelberg.

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14979-5

  • Online ISBN: 978-3-642-14980-1

  • eBook Packages: Computer ScienceComputer Science (R0)