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PUBLICATIONS

Topological Initialization for Multidimensional Scaling

Published

Author(s)

Melinda Kleczynski, Anthony Kearsley

Abstract

Multidimensional scaling is a popular technique for visualizing dissimilarities between objects in complex datasets. For a single dataset, different initial configurations for multidimensional scaling may produce representations with qualitatively different features. This presents both challenges and opportunities for those who use these methods. We introduce the new technique Topological Initialization for Multidimensional Scaling (TIMDS) which employs cycle representatives, a tool from topological data analysis, to generate multidimensional scaling initializations. We show that for some datasets, TIMDS produces representations with competitive stress values and better visualization of key dataset attributes compared to random or classical initialization methods.
Citation
Technical Note (NIST TN) - 2349
Report Number
2349
NIST Pub Series
Technical Note (NIST TN)
Pub Type
NIST Pubs

Download Paper

https://doi.org/10.6028/NIST.TN.2349
Local Download

Keywords

Cycle representatives, multidimensional scaling, topological data analysis.
Mathematics and statistics

Citation

Kleczynski, M. and Kearsley, A. (2025), Topological Initialization for Multidimensional Scaling, Technical Note (NIST TN), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://doi.org/10.6028/NIST.TN.2349, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=960027 (Accessed October 9, 2025)

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Created September 16, 2025
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