Abstract
A basic problem in Multidimensional Scaling is to minimize the weighted sum of squared differences between given dissimilarities and distances over all Euclidian distance matrices. Existing algorithms solve this problem in a not quite satisfactory way. The present paper aims at the development of dual algorithms which are able to find the global minimum with a sufficient speed of convergence.
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© 1991 Springer-Verlag Berlin · Heidelberg
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Mathar, R. (1991). Dual Algorithms in Multidimensional Scaling. In: Bock, HH., Ihm, P. (eds) Classification, Data Analysis, and Knowledge Organization. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-76307-6_14
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DOI: https://doi.org/10.1007/978-3-642-76307-6_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-53483-9
Online ISBN: 978-3-642-76307-6
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