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Proceedings Paper

Fuzzification of the self-organizing feature map: will it work?
Author(s): James C. Bezdek; Nikhil R. Pal
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Paper Abstract

Many authors are currently investigating fuzzification of self-organizing algorithms. This paper discusses some problems that can be expected during attempts to generalize Kohonen's self-organizing feature map (SOFM) as it is used for feature extraction and visual display. We review three methods for solving each of these two problems: principal components analysis; Sammon's algorithm; and Kohonen's SOFM algorithm. Then we present a number of numerical examples that illustrate some difficulties with the SOFM approach. We propose a modification of SOFM that extracts feature vectors in q-space from data in p-space. However, since the coordinates of the extracted points are constrained via logical connectivity to a display lattice in q-space, the resultant features are not particularly good lower dimensional representations of the data they attempt to mimic. Our metric topological preservation index suggests that Extended SOFM does not preserve topological relationships nearly as well as principal components or Sammon's algorithm.

Paper Details

Date Published: 22 December 1993
PDF: 21 pages
Proc. SPIE 2061, Applications of Fuzzy Logic Technology, (22 December 1993); doi: 10.1117/12.165020
Show Author Affiliations
James C. Bezdek, Univ. of West Florida (United States)
Nikhil R. Pal, Indian Statistical Institute (India)


Published in SPIE Proceedings Vol. 2061:
Applications of Fuzzy Logic Technology
Bruno Bosacchi; James C. Bezdek, Editor(s)

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