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Convergences in cognitive science, social network analysis, pattern recognition and machine intelligence as dynamic processes in non-Euclidean space

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  • Joseph Woelfel

    (University at Buffalo, State University of New York)

Abstract

Students of human cognitive and cultural processes, social networks, pattern recognition and machine intelligence often find that the coordinate systems resulting from commonly used measurement and analysis tools yield non-Euclidean configurations. Typically, researchers consider this unfortunate, and seek methods to return the spaces to Euclidean configurations. This article details all the known methods of such transformations, but presents evidence from multiple fields of inquiry that shows the non-Euclidean nature of the space is meaningful, and that all transformations to Euclidean form produce serious distortions to measured values. The article further presents methods for describing processes in the non-Euclidean spaces along with empirical examples of such uses.

Suggested Citation

  • Joseph Woelfel, 2020. "Convergences in cognitive science, social network analysis, pattern recognition and machine intelligence as dynamic processes in non-Euclidean space," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(1), pages 263-278, February.
  • Handle: RePEc:spr:qualqt:v:54:y:2020:i:1:d:10.1007_s11135-019-00852-2
    DOI: 10.1007/s11135-019-00852-2
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    References listed on IDEAS

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    1. Gale Young & A. Householder, 1938. "Discussion of a set of points in terms of their mutual distances," Psychometrika, Springer;The Psychometric Society, vol. 3(1), pages 19-22, March.
    2. Henry Kaiser, 1958. "The varimax criterion for analytic rotation in factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 23(3), pages 187-200, September.
    3. Joseph Woelfel & George Barnett & Robert Pruzek & Robert Zimmelman, 1989. "Rotation to simple processes: the effect of alternative rotation rules on observed patterns in time-ordered measurements," Quality & Quantity: International Journal of Methodology, Springer, vol. 23(1), pages 3-20, February.
    4. Roger Shepard, 1962. "The analysis of proximities: Multidimensional scaling with an unknown distance function. II," Psychometrika, Springer;The Psychometric Society, vol. 27(3), pages 219-246, September.
    5. J. Kruskal, 1964. "Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis," Psychometrika, Springer;The Psychometric Society, vol. 29(1), pages 1-27, March.
    6. Roger Shepard, 1962. "The analysis of proximities: Multidimensional scaling with an unknown distance function. I," Psychometrika, Springer;The Psychometric Society, vol. 27(2), pages 125-140, June.
    7. Joseph Woelfel & George Barnett, 1982. "Multidimensional scaling in Riemann space," Quality & Quantity: International Journal of Methodology, Springer, vol. 16(6), pages 469-491, December.
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