Global Minima by Penalized Full-dimensional Scaling
Computation
2024-07-24 v1 Machine Learning
Abstract
The full-dimensional (metric, Euclidean, least squares) multidimensional scaling stress loss function is combined with a quadratic external penalty function term. The trajectory of minimizers of stress for increasing values of the penalty parameter is then used to find (tentative) global minima for low-dimensional multidimensional scaling. This is illustrated with several one-dimensional and two-dimensional examples.
Keywords
Cite
@article{arxiv.2407.16645,
title = {Global Minima by Penalized Full-dimensional Scaling},
author = {Jan de Leeuw},
journal= {arXiv preprint arXiv:2407.16645},
year = {2024}
}
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39 pages