Tools for Non-linear Data Analysis

Objective
Study and develop novel methods for non-linear data analysis.

Research group members
dr. N. Vlassis
drs. J.J. Verbeek
dr. ir. B. Kröse

Funding
The project is funded by Stichting Technische Wetenschappen

Collaboration
We collaborate with Bob Duin (Delft University of Technology) and industrial partners (Noldus BV, Unilever, KIQ, TNO-FEL)

Motivation
Current computerized measurement systems and data acquisition systems deliver a huge amount of data. For example, in the petrophysical industry more and more advanced measuring devices are used to determine the characteristics of the borehole. Because the sensors are often measuring on the same physical phenomenon (in the above application for example the porosity), the intrinsic dimensionality of the data will in many cases be lower than the dimensionality of the data itself and only depend on the degrees of freedom of the observed phenomenon. If the dimensionality of the measurement space is not reduced correspondingly by some mapping, the outcomes of any analysis of the measurements may suffer from an increased noise resulting from more sensor signals, instead of taking advantage of the increased information or resolution. Feature extraction and feature reduction thereby become more and more important in relation with increasing sensor capabilities. However, standard analysis packages are often limited to linear projections, while the data not necessarily resides on a linear manifold.

Recently, a number of novel promising techniques for nonlinear projections were proposed by the involved groups (Intelligent Autonomous Systems at University of Amsterdam and Pattern Recognition at Delft University). The techniques will be further studied and elaborated, and novel methods will emerge per case. Depending on the application (visualisation, compression or classification) we will define criteria to assess the performance. All methods will be tested on these criteria and on speed.

There is an existing collaboration with the following users: Shell with applications in the analysis of petrophysical and seismic data, TNO-FEL with applications in the classification of radar profiles, Noldus Information Technology with applications in the analysis of behavioural data, KiQ and Cap Gemini with applications in the analysis of time series, and Unilever with applications in visualization of physical processes. To enable an easy and broad utilisation we will implement the developed methods in a toolbox, compatible with a standard data analysis software package (for example Matlab or SPSS). For the exploitation of such a toolbox we could use another user in the users group.
 

Publications

J.J. Verbeek, N. Vlassis, and B. Kröse. Efficient Greedy Learning of Gaussian Mixture Models. Neural Computation 15(1), 2003.

A. Likas, N. Vlassis and J.J. Verbeek. The Global K-Means Clustering Algorithm. Pattern Recognition 36(2), 2003.

J.J. Verbeek, N. Vlassis, and B. Kröse. Coordinating Principal Component Anlyzers. In Proc. Int. Conf. on Artificial Neural Networks, pages 914-919, Madrid, Spain, August 2002.

J.J. Verbeek, N. Vlassis, and B. Kröse. A k-segments algorithm for finding principal curves. Pattern Recognition Letters, Vol. 23(8):1009-1017, 2002.

J.J. Verbeek, N. Vlassis, and B. Kröse. Fast nonlinear dimensionality reduction with topology representing networks. In Proc. 10th European Symposium on Artificial Neural Networks, pages 193-198, Bruges, Belgium, 2002.

D. de Ridder, O. Lemmers, R.P.W. Duin, and J. Kittler. The adaptive subspace map for image description and image database retrieval. In F.J. Ferri, J.M. Iñesta, A. Amin, and P. Pudil, editors, Proceedings of the joint IAPR International Workshops on Syntactical and Structural Pattern Recognition (S+SSPR 2000), pages 94-103,