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Dual Algorithms in Multidimensional Scaling

  • Rudolf Mathar
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

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.

Keywords

Unit Sphere Multidimensional Scaling Homogeneous Function Supporting Hyperplane Dual Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin · Heidelberg 1991

Authors and Affiliations

  • Rudolf Mathar
    • 1
  1. 1.Institute of StatisticsAachen University of TechnologyAachenGermany

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