Visualization of high-dimensional data using two-dimensional self-organizing piecewise-smooth Kohonen maps

To make visualization of high-dimensional data more accurate, we offer a method of approximating two-dimensional Kohonen maps lying in a multiple-dimensional space. Cubic parametric spline-based least-defect surfaces can be used as an approximation function to minimize approximation errors.

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Bibliographic Details
Published in:Optical memory & neural networks 2012, Vol.21 (4), p.227-232
Main Authors: Shklovets, A. V., Axak, N. G.
Format: Article
Language:English
Subjects:
Complexity
Computer Science
Information Storage and Retrieval
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Description
Summary:To make visualization of high-dimensional data more accurate, we offer a method of approximating two-dimensional Kohonen maps lying in a multiple-dimensional space. Cubic parametric spline-based least-defect surfaces can be used as an approximation function to minimize approximation errors.
ISSN:1060-992X
1934-7898
DOI:10.3103/S1060992X12040066