SpringerLink
Log in

Fast Spectral Clustering with Random Projection and Sampling

  • Conference paper
Machine Learning and Data Mining in Pattern Recognition (MLDM 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5632))

  • 2774 Accesses

Abstract

This paper proposes a fast spectral clustering method for large-scale data. In the present method, random projection and random sampling techniques are adopted for reducing the data dimensionality and cardinality. The computation time of the present method is quasi-linear with respect to the data cardinality. The clustering result can be updated with a small computational cost when data samples or random samples are appended or removed.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Hagen, L., Kahng, A.: New spectral methods for ratio cut partitioning and clustering. IEEE Transactions on Computer-Aided Design 11(9), 1074–1085 (1992)

    Article  Google Scholar 

  2. Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 888–905 (2000)

    Article  Google Scholar 

  3. Ding, C.H.Q., He, X., Zha, H., Gu, M., Simon, H.D.: A min-max cut algorithm for graph partitioning and data clustering. In: Proceedings of ICDM 2001, pp. 107–114 (2001)

    Google Scholar 

  4. Ng, A.Y., Jordan, M.I., Weiss, Y.: On spectral clustering: Analysis and an algorithm. In: Advances in Neural Information Processing Systems, vol. 14, pp. 849–856. MIT Press, Cambridge (2001)

    Google Scholar 

  5. Fowlkes, C., Belongie, S., Chung, F., Malik, J.: Spectral grou** using the Nyström method. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 214–225 (2004)

    Article  Google Scholar 

  6. Drineas, P., Mahoney, M.W.: On the Nyström method for approximating a gram matrix for improved kernel-based learning. Journal of Machine Learning Research 6, 2153–2175 (2005)

    MathSciNet  MATH  Google Scholar 

  7. Freitas, N.D., Wang, Y., Mahdaviani, M., Lang, D.: Fast Krylov methods for N-body learning. In: Advances in Neural Information Processing Systems, vol. 18, pp. 251–258. MIT Press, Cambridge (2006)

    Google Scholar 

  8. Song, Y., Chen, W.Y., Bai, H., Lin, C.J., Chang, E.Y.: Parallel spectral clustering. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008, Part II. LNCS, vol. 5212, pp. 374–389. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  9. Mahadevan, S.: Fast spectral learning using Lanczos eigenspace projections. In: AAAI, pp. 1472–1475 (2008)

    Google Scholar 

  10. Golub, G.H., Loan, C.F.V.: Matrix Computations, 3rd edn. Johns Hopkins University Press (1996)

    Google Scholar 

  11. Williams, C.K.I., Seeger, M.: Using the Nyström method to speed up kernel machines. In: Advances in Neural Information Processing Systems, vol. 13, pp. 682–688. MIT Press, Cambridge (2001)

    Google Scholar 

  12. Von Luxburg, U.: A tutorial on spectral clustering. Statistics and Computing 17(4), 395–416 (2007)

    Article  MathSciNet  Google Scholar 

  13. Fiedler, M.: Algebraic connectivity of graphs. Czechoslovak Mathematical Journal 23, 298–305 (1973)

    MathSciNet  MATH  Google Scholar 

  14. Fiedler, M.: A property of eigenvectors of nonnegative symmetric matrices and its application to graph theory. Czechoslovak Mathematical Journal 25, 619–633 (1975)

    Article  MathSciNet  MATH  Google Scholar 

  15. Scott, G.L., Longuet-Higgins, H.C.: Feature grou** by relocalisation of eigenvectors of the proximity matrix. In: British Machine Vision Conference, pp. 103–108 (1990)

    Google Scholar 

  16. Zelnik-Manor, L., Perona, P.: Self-tuning spectral clustering. In: Advances in Neural Information Processing Systems, vol. 17, pp. 1601–1608. MIT Press, Cambridge (2004)

    Google Scholar 

  17. Johnson, W., Lindenstrauss, J.: Extensions of Lipschitz maps into a Hilbert space. Contemporary Mathematics 26, 189–206 (1984)

    Article  MATH  Google Scholar 

  18. Dasgupta, S., Gupta, A.: An elementary proof of the Johnson-Lindenstrauss lemma. Technical report, UC Berkeley (1999)

    Google Scholar 

  19. Achlioptas, D.: Database-friendly random projections: Johnson-Lindenstrauss with binary coins. Journal of Computer and System Sciences 66, 671–687 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  20. Brigham, E., Maninila, H.: Random projection in dimensionality reduction: applications to image and text data. In: ACM SIGKDD ICKDDM, pp. 245–250 (2001)

    Google Scholar 

  21. Fradkin, D., Madigan, D.: Experiments with random projections for machine learning. In: ACM SIGKDD ICKDDM, pp. 517–522 (2003)

    Google Scholar 

  22. Achlioptas, D., Mcsherry, F., Olkopf, B.S.: Sampling techniques for kernel methods. In: Annual Advances in Neural Information Processing Systems, vol. 14, pp. 335–342. MIT Press, Cambridge (2002)

    Google Scholar 

  23. Dhillon, I.S.: Co-clustering documents and words using bipartite spectral graph partitioning. In: ACM SIGKDD, pp. 269–274. ACM, New York (2001)

    Google Scholar 

  24. Gu, M., Eisenstat, S.C.: A stable and fast algorithm for updating the singular value decomposition. Tech. Rep. YALEU/DCS/RR-966, Yale University (1994)

    Google Scholar 

  25. Berry, M.W.: Large scale sparse singular value computations. International Journal of Supercomputer Applications 6, 13–49 (1992)

    Google Scholar 

  26. Bunch, J.R., Nielsen, C.P.: Updating the singular value decomposition. Numerische Mathematik 31, 111–129 (1978)

    Article  MathSciNet  MATH  Google Scholar 

  27. Gu, M., Eisenstat, S.C.: A stable and fast algorithm for updating the singular value decomposition. Tech. Rep. YALEU/DCS/RR-966, Yale University (1994)

    Google Scholar 

  28. Chandrasekaran, S., Manjunath, B.S., Wang, Y.F., Winkeler, J., Zhang, H.: An eigenspace update algorithm for image analysis. Graphical models and image processing 59(5), 321–332 (1997)

    Article  Google Scholar 

  29. Brand, M.: Incremental singular value decomposition of uncertain data with missing values. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 707–720. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  30. Brand, M.: Fast online SVD revisions for lightweight recommender systems. In: SIAM International Conference on Data Mining, pp. 37–46 (2003)

    Google Scholar 

  31. Skocaj, D., Leonardis, A.: Weighted and robust incremental method for subspace learning. In: Proc. ICCV 2003, vol. 2, p. 1494 (2003)

    Google Scholar 

  32. Davies, P.I., Smith, M.I.: Updating the singular value decomposition. Journal of Computational and Applied Mathematics 170, 145–167 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  33. Skocaj, D., Leonardis, A.: Incremental and robust learning of subspace representations. Image and Vision Computing 26, 27–38 (2008)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Media and Information Technology, Chiba University, Japan

    Tomoya Sakai & Atsushi Imiya

Authors
  1. Tomoya Sakai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Atsushi Imiya
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institut für Bildverarbeitung und angewandte Informatik, Körnerstr. 10, 04107, Leipzig, Deutschland, Germany

    Petra Perner

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Sakai, T., Imiya, A. (2009). Fast Spectral Clustering with Random Projection and Sampling. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2009. Lecture Notes in Computer Science(), vol 5632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03070-3_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-03070-3_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03069-7

  • Online ISBN: 978-3-642-03070-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation