Skip to main content
SpringerLink
Log in
Book cover

Pacific-Rim Symposium on Image and Video Technology

PSIVT 2006: Advances in Image and Video Technology pp 118–127Cite as

LPP and LPP Mixtures for Graph Spectral Clustering

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4319))

Abstract

In this paper, we concentrate on graph clustering by using graph spectral features. The leading eigenvectors or the spectrum of graphs and derived feature inter-mode adjacency matrix are used. The embedding methods are the Locality Preserving Projection(LPP) and the mixtures of LPP. The experiment results show that although both of the conventional LPP and the LPP mixtures can separate the different graphs into outstanding clusters, the conventional LPP outperforms the LPP mixtures in the sense of compactness for graph clustering.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bunke, H.: Error correcting graph matching: On the influence of the underlying cost function. IEEE Transactions on Pattern Analysis and Machine Intelligence 21, 917–922 (1999)

    Article  Google Scholar 

  2. Chen, S., Zhao, H., Luo, B.: Lpp/qr for under-sampled image recognition. In: IEEE Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, pp. 4558–4563 (2005)

    Google Scholar 

  3. Christmas, W.J., Kittler, J., Petrou, M.: Structural matching in computer vision using probabilistic relaxation. IEEE PAMI 17(8), 749–764 (1995)

    Google Scholar 

  4. Cyr, C.M., Kimia, B.B.: 3D Object Recognition Using Shape Similarity-Based Aspect Graph. In: ICCV 2001, pp. I: 254–261 (2001)

    Google Scholar 

  5. Davies, D., Bouldin, D.: A cluster seperation measure. IEEE T-PAMI 1(2), 224–227 (1979)

    Google Scholar 

  6. Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum-likelihood from incomplete data via the EM algorithm. J. Royal Statistical Soc. Ser. B (methodological) 39, 1–38 (1977)

    MATH  MathSciNet  Google Scholar 

  7. Eshera, M.A., Fu, K.S.: An image understanding system using attributed symbolic representation and inexact graph-matching. Journal of the Association for Computing Machinery 8(5), 604–618 (1986)

    Google Scholar 

  8. He, X., Niyogi, P.: Locality preserving projections. In: Proc. Conf. Advances in Neural Information Processing Systems (2003)

    Google Scholar 

  9. He, X., Yan, S.C., Hu, Y., Niyogi, P., Zhang, H.J.: Face recognition using laplacianfaces. IEEE T-PAMI 27(3), 328–340 (2005)

    Google Scholar 

  10. He, X., Yan, S., Hu, Y., Niyogi, P., Zhang, H.: Face recognition using laplacianfaces. IEEE Trans. Pattern Anal. Mach. Intell. 27(3), 328–340 (2005)

    Article  Google Scholar 

  11. Dunn, J.: Well seperated clusers and optimal fuzzy partitions. J. Cybernet 4, 95–104 (1974)

    Article  MathSciNet  Google Scholar 

  12. Hubert, L., Schultz, J.: Quadratic assignment as a general data-analysis strategy. Br. J. Math. Stat. Psychol. 29, 190–241 (1976)

    MATH  MathSciNet  Google Scholar 

  13. Luo, B., Wilson, R.C., Hancock, E.R.: Spectral embedding of graphs. Pattern Recognition 36(10), 2213–2230 (2003)

    Article  MATH  Google Scholar 

  14. Tipping, M., Bishop, C.: Mixtures of probabilistic principal component analysis. Neural Computating 11, 443–482 (1999)

    Article  Google Scholar 

  15. Rousseeuw, P.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20(1), 53–65 (1987)

    Article  MATH  Google Scholar 

  16. Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323–2326 (2000)

    Article  Google Scholar 

  17. Sanfeliu, A., Fu, K.S.: A distance measure between attributed relational graphs for pattern recognition. IEEE Transactions Systems, Man and Cybernetics 13(3), 353–362 (1983)

    MATH  Google Scholar 

  18. Sengupta, K., Boyer, K.L.: Organizing large structural modelbases. PAMI 17(4), 321–332 (1995)

    Google Scholar 

  19. Torsello, A., Hancock, E.R.: Efficiently computing weighted tree edit distance using relaxation labeling. In: Figueiredo, M., Zerubia, J., Jain, A.K. (eds.) EMMCVPR 2001. LNCS, vol. 2134, pp. 438–453. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  20. Wilson, R.C., Hancock, E.R.: Structural matching by discrete relaxation. IEEE T-PAMI 19(6), 634–648 (1997)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Key Lab of Intelligent Computing & Signal Processing of Ministry of Education, Anhui University, Hefei, Anhui, 230039, China

    Bin Luo & Sibao Chen

Authors
  1. Bin Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sibao Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science, National Tsing Hua University, HsinChu, Taiwan

    Long-Wen Chang

  2. Department of Electrical Engineering, National Chung Cheng University, 621, Chia-Yi, Taiwan, ROC

    Wen-Nung Lie

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Luo, B., Chen, S. (2006). LPP and LPP Mixtures for Graph Spectral Clustering. In: Chang, LW., Lie, WN. (eds) Advances in Image and Video Technology. PSIVT 2006. Lecture Notes in Computer Science, vol 4319. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11949534_12

Download citation

  • DOI: https://doi.org/10.1007/11949534_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-68297-4

  • Online ISBN: 978-3-540-68298-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation