Abstract
A plethora of technical problems are characterized by sets of high-dimensional data. Significance, correlations, redundancy, or irrelevancy of the variables v i with regard to the given application are a priori unknown. The extraction of underlying knowledge or the reliable automatic classification for cognitive systems requires the reduction of the initial data set to the essential information and the corresponding variables. Thus, dimensionality reduction is an ubiquituous problem and together with multivariate data visualization a topic of interdisciplinary research interest for more than three decades. Recently, high economic interest applications, e.g., data mining and knowledge discovery applications, give renewed strong incentive to the field. This tutorial, gives a focused survey of relevant methods from past to present based on an elaborated taxonomy. Quantitative and qualitative assessment and comparison of the methods in an unifying approach will be carried out. Enhancements and benefits of interactive visualization will be introduced. Dedicated tools based on the introduced method spectrum will be presented and key applications will serve for further elucidation of the approach and its potential. The most practical methods and tools from the tutorial as well as a comprehensive sets of slides are available from http://www.iee.et.tu-dresden.de/~koeniga/QuickCog.html as free demo software version of the QuickCog system.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
König, A. (2001) Dimensionality Reduction Techniques for Interactive Visualisation, Exploratory Data Analysis, and Classification.. In Pattern Recognition in Soft Computing Paradigm, “Nikhil R. Pal (ed.)”, Vol. 2, Chapter 1, “World Scientific, FLSI Soft Computing Series”, Singapore, pp. 1–37
König, A. (2000) Dimensionality Reduction Techniques for Multivariate Data Classification, Interactive Visualization, and Analysis — Systematic Feature Selection vs. Extraction. In Proc. of 4th Int. Conf. on Knowledge-Based Intelligent Engineering Systems ℰ Allied Technologies KES’2000, University of Brighton, UK, pp.44–56
König, A. (2000) Interactive Visualization and Analysis of Hierarchical Neural Projections for Data Mining.. In IEEE Transactions on Neural Networks TNN Special Issue on Data Mining, Vol.11, pp. 615–624.
Fukunaga, K. (1990) Introduction to Statistical Pattern Recognition.. ACADEMIC PRESS, INC. Harcourt Brace Jovanovich, Publishers Boston San Diego New York London Sydney Tokyo Toronto.
Kittler, J. (1986) Feature Selection and Extraction.. In Handbook of Pattern Recognition and Image Processing, ACADEMIC PRESS, INC. Tzai. Y. Young King Sun-Fu, Publishers, Orlando San Diego New York Austin London Montreal Sydney Tokyo Toronto, pp. 59–83
Raymer, M.L., Punch, W.F., Goodman, E.D., Kuhn, L.A., Jain, A.K. (2000) Dimensionality Reduction Using Genetic Algorithms.. In IEEE Transactions on Evolutionary Computation, Vol.4, No.2, pp. 164–171.
Köhler, C., König, Temelkova-Kurktschiev, T., Hanefeld, M. (1999) Application of Interactive Multivariate Data Visualisation to the Analysis of Patients Findings in Metabolic Research. In Proc. of the 3rd Int. Conf. on Knowledge-Based Intelligent Information Engineering Systems KES’99, Adelaide, Australia, pp. 397–402
Sammon, J.W. (1970) Interactive Pattern Analysis and Classification.. In IEEE Transactions on Computers, C-19, No.7, pp. 594–616.
Sammon, J.W. (1969) A Nonlinear Mapping for Data Structure Analysis.. In IEEE Transactions on Computers, C-18, No.7, pp. 401–409.
Fukunaga, K., Koontz, W.L.G. (1972) A Nonlinear Feature Extractioll Algorithm Using Distance Transformation.. In IEEE Transactions on Computers C-21, pp. 56–63.
Kohonen, T. (1989) Self-Organization and Associative Memory.. Springer Verlag Berlin Heidelberg London Paris Tokyo Hong Kong.
König, A., Blutner, F.E., Eberhardt, M., and Wenzel R. (2000) Design and Application of an Acoustic Data Base Navigator for the Interactive Analysis of Psycho-Acoustic Sound Archives and Sound Engineering. In Advanced Signal Processing Technology by Soft Computing, “Charles Hsu (ed.)”, Vol. 1, Chapter 1, “World Scientific, FLSI Soft Computing Series”, Singapore, pp. 36–65.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag London
About this chapter
Cite this chapter
König, A. (2002). Dimensionality Reduction and Interactive Visualization of Multivariate Data — Methods, Tools, Applications. In: Roy, R., Köppen, M., Ovaska, S., Furuhashi, T., Hoffmann, F. (eds) Soft Computing and Industry. Springer, London. https://doi.org/10.1007/978-1-4471-0123-9_39
Download citation
DOI: https://doi.org/10.1007/978-1-4471-0123-9_39
Publisher Name: Springer, London
Print ISBN: 978-1-4471-1101-6
Online ISBN: 978-1-4471-0123-9
eBook Packages: Springer Book Archive