Guiding Local Regression Using Visualisation

  • Dharmesh M. Maniyar
  • Ian T. Nabney
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3635)


Solving many scientific problems requires effective regression and/or classification models for large high-dimensional datasets. Experts from these problem domains (e.g. biologists, chemists, financial analysts) have insights into the domain which can be helpful in developing powerful models but they need a modelling framework that helps them to use these insights. Data visualisation is an effective technique for presenting data and requiring feedback from the experts. A single global regression model can rarely capture the full behavioural variability of a huge multi-dimensional dataset. Instead, local regression models, each focused on a separate area of input space, often work better since the behaviour of different areas may vary. Classical local models such as Mixture of Experts segment the input space automatically, which is not always effective and it also lacks involvement of the domain experts to guide a meaningful segmentation of the input space. In this paper we addresses this issue by allowing domain experts to interactively segment the input space using data visualisation. The segmentation output obtained is then further used to develop effective local regression models.


Input Space Domain Expert Local Regression Synthetic Dataset Normalise Mean Square Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Jacobs, R.A., Jordan, M.I., Nowlan, S.J., Hinton, G.E.: Adaptive mixture of local experts. Neural Computation 3, 79–87 (1991)CrossRefGoogle Scholar
  2. 2.
    Tiňo, P., Nabney, I.T.: Constructing localized non-linear projection manifolds in a principled way: hierarchical generative topographic mapping. IEEE T. Pattern Analysis and Machine Intelligence 24, 639–656 (2002)CrossRefGoogle Scholar
  3. 3.
    Bishop, C.M., Svensén, M., Williams, C.K.I.: GTM: The generative topographic mapping. Neural Computation 10, 215–234 (1998)CrossRefGoogle Scholar
  4. 4.
    Aurenhammer, F.: Voronoi diagrams - survey of a fundamental geometric data structure. ACM Computing Surveys 3, 345–405 (1991)CrossRefGoogle Scholar
  5. 5.
    Bishop, C.M., Svensén, M., Williams, C.K.I.: Magnification factors for the GTM algorithm. In: Proceedings IEE Fifth International Conference on Artificial Neural Networks, pp. 64–69 (1997)Google Scholar
  6. 6.
    Tiňo, P., Nabney, I.T., Sun, Y.: Using directional curvatures to visualize folding patterns of the GTM projection manifolds. In: Dorffner, G., Bischof, H., Hornik, K. (eds.) ICANN 2001. LNCS, vol. 2130, pp. 421–428. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  7. 7.
    Ellis, R.: Entropy, Large Deviations, and Statistical Mechanics. Springer, New York (1985)zbMATHGoogle Scholar
  8. 8.
    Bishop, C.M.: Neural Networks for Pattern Recognition, 1st edn. Oxford University Press, Oxford (1995)Google Scholar
  9. 9.
    Weiss, N.A.: Elementary Statistics, 3rd edn. Addison Wesley, Reading (1996)Google Scholar
  10. 10.
    Good, A.C., Krystek, S.R., Mason, J.S.: High-throughput and virtual screening: core lead discovery techologies move towards integration. Drug Discovery Today 5, S61–S69 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Dharmesh M. Maniyar
    • 1
  • Ian T. Nabney
    • 1
  1. 1.Neural Computing Research GroupAston UniversityBirminghamUK

Personalised recommendations