Professor Jochen Einbeck jochen.einbeck@durham.ac.uk
Professor
Exploring Multivariate Data Structures with Local Principal Curves
Einbeck, Jochen; Tutz, Gerhard; Evers, Ludger
Authors
Gerhard Tutz
Ludger Evers
Contributors
C. Weihs
Editor
W. Gaul
Editor
Abstract
A new approach to find the underlying structure of a multidimensional data cloud is proposed, which is based on a localized version of principal components analysis. More specifically, we calculate a series of local centers of mass and move through the data in directions given by the first local principal axis. One obtains a smooth ``local principal curve'' passing through the "middle" of a multivariate data cloud. The concept adopts to branched curves by considering the second local principal axis. Since the algorithm is based on a simple eigendecomposition, computation is fast and easy.
Citation
Einbeck, J., Tutz, G., & Evers, L. (2005). Exploring Multivariate Data Structures with Local Principal Curves. In C. Weihs, & W. Gaul (Eds.), Proceedings of the 28th Annual Conference of the Gesellschaft für Klassifikation, 9-11 March 2004, University of Dortmund (256-263)
Conference Name | 28th Annual Conference of the German Classiciation Society. |
---|---|
Conference Location | Magedeburg, Germany |
Publication Date | Jan 1, 2005 |
Deposit Date | Feb 2, 2009 |
Publicly Available Date | Apr 8, 2009 |
Publisher | Springer Verlag |
Pages | 256-263 |
Series Title | Studies in classification data analysis and knowledge organization |
Series Number | 28 |
Book Title | Proceedings of the 28th Annual Conference of the Gesellschaft für Klassifikation, 9-11 March 2004, University of Dortmund. |
Public URL | https://durham-repository.worktribe.com/output/1162294 |
Publisher URL | http://www.springer.com/computer/security+and+cryptology/book/978-3-540-25677-9 |
Files
Accepted Conference Proceeding
(293 Kb)
PDF
Copyright Statement
The original publication is available at www.springerlink.com
You might also like
Parents and Children Together (PACT) Evaluation Report
(2022)
Report
Using Random Effect Models to Produce Robust Estimates of Death Rates in COVID-19 Data
(2022)
Journal Article