Tools for Non-linear Data Analysis
Study and develop novel methods for non-linear data analysis.
Research group members
dr. N. Vlassis
drs. J.J. Verbeek
dr. ir. B. Kröse
The project is funded by Stichting Technische
We collaborate with Bob Duin (Delft University of Technology) and industrial partners
(Noldus BV, Unilever, KIQ, TNO-FEL)
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.
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.
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,