Multidimensional Scaling and Genetic Algorithms : A Solution Approach to Avoid Local Minima
Multidimensional scaling is very common in exploratory data analysis. It is mainly used to represent sets of objects with respect to their proximities in a low dimensional Euclidean space. Widely used optimization algorithms try to improve the representation via shifting its coordinates in direction of the negative gradient of a corresponding fit function. Depending on the initial configuration, the chosen algorithm and its parameter settings there is a possibility for the algorithm to terminate in a local minimum. This article describes the combination of an evolutionary model with a non-metric gradient solution method to avoid this problem. Furthermore a simulation study compares the results of the evolutionary approach with one classic solution method.
|Date of creation:||2002|
|Date of revision:|
|Contact details of provider:|| Web page: http://www.wiwi.uni-augsburg.de/bwl/bamberg/|
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Richard Johnson, 1973. "Pairwise nonmetric multidimensional scaling," Psychometrika, Springer, vol. 38(1), pages 11-18, March.
- J. Kruskal, 1964. "Nonmetric multidimensional scaling: A numerical method," Psychometrika, Springer, vol. 29(2), pages 115-129, June.
- J. Kruskal, 1964. "Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis," Psychometrika, Springer, vol. 29(1), pages 1-27, March.
- Jacqueline Meulman & Peter Verboon, 1993. "Points of view analysis revisited: Fitting multidimensional structures to optimal distance components with cluster restrictions on the variables," Psychometrika, Springer, vol. 58(1), pages 7-35, March.
When requesting a correction, please mention this item's handle: RePEc:zbw:augamw:181. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (ZBW - German National Library of Economics)
If references are entirely missing, you can add them using this form.