Dual Algorithms in Multidimensional Scaling
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.
KeywordsUnit Sphere Multidimensional Scaling Homogeneous Function Supporting Hyperplane Dual Algorithm
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