Skip to main content
SpringerLink
Log in

Centering Versus Scaling for Hubness Reduction

  • Conference paper
  • First Online:
Artificial Neural Networks and Machine Learning – ICANN 2016 (ICANN 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9886))

Included in the following conference series:

Abstract

Hubs and anti-hubs are points that appear very close or very far to many other data points due to a problem of measuring distances in high-dimensional spaces. Hubness is an aspect of the curse of dimensionality affecting many machine learning tasks. We present the first large scale empirical study to compare two competing hubness reduction techniques: scaling and centering. We show that scaling consistently reduces hubness and improves nearest neighbor classification, while centering shows rather mixed results. Support vector classification is mostly unaffected by centering-based hubness reduction.

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

Access this chapter

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

Institutional subscriptions

Notes

  1. 1.

    Python scripts for hubness analysis are available at: https://github.com/OFAI.

References

  1. Flexer, A.: Improving visualization of high-dimensional music similarity spaces. In: 16th ISMIR Conference (2015)

    Google Scholar 

  2. Flexer, A., Schnitzer, D., Schlüter, J.: A MIREX meta-analysis of hubness in audio music similarity. In: 13th ISMIR Conference (2012)

    Google Scholar 

  3. Francois, D., Wertz, V., Verleysen, M.: The concentration of fractional distances. IEEE Trans. Knowl. Data Eng. 19, 873–886 (2007)

    Article  Google Scholar 

  4. Hara, K., Suzuki, I., Shimbo, M., Kobayashi, K., Fukumizu, K., Radovanović, M.: Localized centering: reducing hubness in large-sample data hubness in high-dimensional data. In: 29th AAAI Conference on Artificial Intelligence, pp. 2645–2651 (2015)

    Google Scholar 

  5. Jegou, H., Harzallah, H., Schmid, C.: A contextual dissimilarity measure for accurate and efficient image search. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2007)

    Google Scholar 

  6. Radovanović, M., Nanopoulos, A., Ivanović, M.: Reverse nearest neighbors in unsupervised distance-based outlier detection. IEEE Trans. Knowl. Data Eng. 27(5), 1369–1382 (2015)

    Article  Google Scholar 

  7. Radovanović, M., Nanopoulos, A., Ivanović, M.: Hubs in space: popular nearest neighbors in high-dimensional data. J. Mach. Learn. Res. 11, 2487–2531 (2010)

    MathSciNet  MATH  Google Scholar 

  8. Schnitzer, D., Flexer, A.: The unbalancing effect of hubs on K-medoids clustering in high-dimensional spaces. In: International Joint Conference on Neural Networks (2015)

    Google Scholar 

  9. Schnitzer, D., Flexer, A., Schedl, M., Widmer, G.: Local and global scaling reduce hubs in space. J. Mach. Learn. Res. 13, 2871–2902 (2012)

    MathSciNet  MATH  Google Scholar 

  10. Schnitzer, D., Flexer, A., Tomašev, N.: A case for hubness removal in high-dimensional multimedia retrieval. In: de Rijke, M., Kenter, T., de Vries, A.P., Zhai, C.X., de Jong, F., Radinsky, K., Hofmann, K. (eds.) ECIR 2014. LNCS, vol. 8416, pp. 687–692. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  11. Suzuki, I., Hara, K., Shimbo, M., Saerens, M., Fukumizu, K.: Centering similarity measures to reduce hubs. In: Conference on Empirical Methods in Natural Language Processing (EMNLP 2013), pp. 613–623 (2013)

    Google Scholar 

  12. Tomašev, N., Radovanović, M., Mladenić, D., Ivanović, M.: The role of hubness in clustering high-dimensional data. IEEE Trans. Knowl. Data Eng. 26(3), 739–751 (2014)

    Article  Google Scholar 

  13. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

Download references

Acknowledgments

This research is supported by the Austrian Science Fund (FWF): P27082, P27703.

Author information

Authors and Affiliations

  1. Austrian Research Institute for Artificial Intelligence (OFAI), Freyung 6/6/7, 1010, Vienna, Austria

    Roman Feldbauer & Arthur Flexer

Authors
  1. Roman Feldbauer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arthur Flexer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Roman Feldbauer .

Editor information

Editors and Affiliations

  1. University of Lausanne, Lausanne, Switzerland

    Alessandro E.P. Villa

  2. University of Lausanne, Lausanne, Switzerland

    Paolo Masulli

  3. Universitat Politécnica de Catalunya, Terrrassa, Spain

    Antonio Javier Pons Rivero

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Feldbauer, R., Flexer, A. (2016). Centering Versus Scaling for Hubness Reduction. In: Villa, A., Masulli, P., Pons Rivero, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science(), vol 9886. Springer, Cham. https://doi.org/10.1007/978-3-319-44778-0_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-44778-0_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44777-3

  • Online ISBN: 978-3-319-44778-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation