OpenAIRE is about to release its new face with lots of new content and services.
During September, you may notice downtime in services, while some functionalities (e.g. user registration, login, validation, claiming) will be temporarily disabled.
We apologize for the inconvenience, please stay tuned!
For further information please contact helpdesk[at]openaire.eu

fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Schroeder, Martin; Nabney, Ian T.; Cornford, Dan (2008)
Publisher: Aston University
Languages: English
Types: Book
Subjects:
Visualising data for exploratory analysis is a big challenge in scientific and engineering domains where there is a need to gain insight into the structure and distribution of the data. Typically, visualisation methods like principal component analysis and multi-dimensional scaling are used, but it is difficult to incorporate prior knowledge about structure of the data into the analysis. In this technical report we discuss a complementary approach based on an extension of a well known non-linear probabilistic model, the Generative Topographic Mapping. We show that by including prior information of the covariance structure into the model, we are able to improve both the data visualisation and the model fit.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] C. M. Bishop, M. Svensen, and C. K. I. Williams. Developments of the generative topographic mapping. Neurocomputing, 21:203-224, 1998.
    • [2] I. Borg and P Groenen. Modern Multidimensional Scaling: theory and applications. Springer-Verlag New York, 2005.
    • [3] D. Broomhead and D. Lowe. Feed-forward neural networks and topographic mappings for exploratory data analysis. Complex Systems 2, pages 321-355, 1988.
    • [4] C. Chatfield and A.J. Collins. Introduction to Multivariate Analysis. Chapman and Hall, 1980.
    • [5] A. Dempster, N. Laird, and D. Rubin. Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society, Vol. 39:1-38, 1977.
    • [6] Stefan Harmeling. Exploring model selection techniques for nonlinear dimensionality reduction. Technical report, Edinburgh University, Scotland, 2007.
    • [7] V. de Silva J.B. Tenenbaum and J.C. Langford. A global geometric framework for nonlinear dimensionality reduction. Science, 290:2319-2323, 2000.
    • [8] Merrill W. Liechty John C. Liechty and Peter Mller. Bayesian correlation estimation. Biometrika, 91:1-14, 2004.
    • [9] T. Kohonen. Self-Organizing Maps . Springer Verlag, 1995.
    • [10] Neil D. Lawrence. A scaled conjugate gradient algorithm for fast supervised learning. Journal of Machine Learning Research 6, page 1783?1816, 2005.
    • [12] S.T. Roweis and L.K. Saul. Locally linear embedding. Science, 290:2323-2326, 2000.
    • [13] Chong Ho Yu. Resampling methods: concepts, applications, and justification. Practical Assessment, Research and Evaluation, 8, 2003.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article

Cookies make it easier for us to provide you with our services. With the usage of our services you permit us to use cookies.
More information Ok