Abstract
Multidimensional scaling is very widely used 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 nonmetric gradient solution method to avoid this problem. Furthermore a simulation study compares the results of the evolutionary approach with one classic solution method.
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Etschberger, S., Hilbert, A. (2003). Evolutionary Strategies to Avoid Local Minima in Multidimensional Scaling. In: Schader, M., Gaul, W., Vichi, M. (eds) Between Data Science and Applied Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18991-3_24
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DOI: https://doi.org/10.1007/978-3-642-18991-3_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40354-8
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