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Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR)

SSPR /SPR 2012: Structural, Syntactic, and Statistical Pattern Recognition pp 33–41Cite as

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  2. Structural, Syntactic, and Statistical Pattern Recognition
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Information Theoretic Prototype Selection for Unattributed Graphs

Information Theoretic Prototype Selection for Unattributed Graphs

  • Lin Han24,
  • Luca Rossi25,
  • Andrea Torsello25,
  • Richard C. Wilson24 &
  • …
  • Edwin R. Hancock24 
  • Conference paper
  • 2405 Accesses

  • 3 Citations

Part of the Lecture Notes in Computer Science book series (LNIP,volume 7626)

Abstract

In this paper we propose a prototype size selection method for a set of sample graphs. Our first contribution is to show how approximate set coding can be extended from the vector to graph domain. With this framework to hand we show how prototype selection can be posed as optimizing the mutual information between two partitioned sets of sample graphs. We show how the resulting method can be used for prototype graph size selection. In our experiments, we apply our method to a real-world dataset and investigate its performance on prototype size selection tasks.

Keywords

  • Prototype Selection
  • Mutual information
  • Importance Sampling
  • Partition function

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References

  1. Han, L., Wilson, R.C., Hancock, E.R.: A Supergraph-based Generative Model. In: ICPR, pp. 1566–1569 (2010)

    Google Scholar 

  2. Torsello, A.: An Importance Sampling Approach to Learning Structural Representations of Shape. In: CVPR, pp. 1–7 (2008)

    Google Scholar 

  3. Buhmann, J.M., Chehreghani, M.H., Frank, M., Streich, A.P.: Information Theoretic Model Selection for Pattern Analysis. JMLR: Workshop and Conference Proceedings 7, 1–15 (2011)

    Google Scholar 

  4. Buhmann, J.M.: Information Thereotic Model Validation for Clustering. In: International Symposium on Information Theory, pp. 1398–1402 (2010)

    Google Scholar 

  5. Nene, S.A., Nayar, S.K., Murase, H.: Columbia Object Image Library(COIL100). Columbia University (1996)

    Google Scholar 

  6. National Center for Biotechnology Information, http://www.ncbi.nlm.nih.gov

  7. Hammersley, J.M., Handscomb, D.C.: Monte Carlo Methods. Wiley, New York (1964)

    CrossRef  MATH  Google Scholar 

  8. Han, L., Hancock, E.R., Wilson, R.C.: Learning Generative Graph Prototypes Using Simplified von Neumann Entropy. In: GbRPR, p. 4251 (2011)

    Google Scholar 

  9. Rissanen, J.: Modelling by Shortest Data Description. Automatica 14, 465–471 (1978)

    CrossRef  MATH  Google Scholar 

  10. Schwarz, G.E.: Estimating the dimension of a model. Annals of Statistics 6, 461–464 (1978)

    CrossRef  MathSciNet  MATH  Google Scholar 

  11. Foster, D.P., George, E.I.: The Risk Inflation Criterion for Multiple Regression. Annals of Statistics 22, 1947–1975 (1994)

    CrossRef  MathSciNet  MATH  Google Scholar 

  12. White, D., Wilson, R.C.: Parts based generative models for graphs. In: ICPR, pp. 1–4 (2008)

    Google Scholar 

  13. Akaike, H.: A new look at the statistical model identification. IEEE Transactions on Automatic Control 19, 716–723 (1974)

    CrossRef  MathSciNet  MATH  Google Scholar 

  14. Grnwald, P.D., Myung, I.J., Pitt, M.A.: Advances in Minimum Description Length: Theory and Applications. The MIT Press (2005)

    Google Scholar 

  15. Luo, B., Hancock, E.R.: Structural graph matching using the EM alogrithm and singular value decomposition. IEEE Transactions on PAMI 23, 1120–1136 (2001)

    CrossRef  Google Scholar 

  16. Sinkhorn, R.: A Relationship Between Arbitrary Positive Matrices and Doubly Stochastic Matrices. The Annals of Mathematical Statistics 35, 876–879 (1964)

    CrossRef  MathSciNet  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of York, UK

    Lin Han, Richard C. Wilson & Edwin R. Hancock

  2. Department of Environmental Science, Informatics and Statistics, Ca’ Foscari Univerisity of Venice, Italy

    Luca Rossi & Andrea Torsello

Authors
  1. Lin Han
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  2. Luca Rossi
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  3. Andrea Torsello
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  4. Richard C. Wilson
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  5. Edwin R. Hancock
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Auckland, Private Bag 92019, 1142, Auckland, New Zealand

    Georgy Gimel’farb

  2. Department of Computer Science, University of York, Deramore Lane, YO10 5GH, York, UK

    Edwin Hancock

  3. Institute of Media and Information Technology, Chiba University, Yayoi-cho 1-33, 263-8522, Inage-ku, Chiba, Japan

    Atsushi Imiya

  4. Technische Universität/Fraunhofer IGD, Fraunhoferstraße 5, 64283, Darmstadt, Germany

    Arjan Kuijper

  5. Graduate School of Information Science and Technology, Hokkaido University, 060-0814, Sapporo, Japan

    Mineichi Kudo

  6. Graduate School of Engineering, Tohoku University, 6-6-05 Aoba, Aramaki, Aoba-ku, 980-8579, Sendai, Miyagi, Japan

    Shinichiro Omachi

  7. Centre for Vision, Speech and Signal Processing, University of Surrey, GU2 7XH, Guildford, Surrey, UK

    Terry Windeatt

  8. C&C Innovation Research Laboratories, NEC Corporation, 8916-47 Takayama-cho, Ikoma-Shi, Nara, Japan

    Keiji Yamada

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© 2012 Springer-Verlag Berlin Heidelberg

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Cite this paper

Han, L., Rossi, L., Torsello, A., Wilson, R.C., Hancock, E.R. (2012). Information Theoretic Prototype Selection for Unattributed Graphs. In: Gimel’farb, G., et al. Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2012. Lecture Notes in Computer Science, vol 7626. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34166-3_4

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  • DOI: https://doi.org/10.1007/978-3-642-34166-3_4

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  • Print ISBN: 978-3-642-34165-6

  • Online ISBN: 978-3-642-34166-3

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