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Proceedings Paper

Pseudo k-means approach to the classifying problem
Author(s): Chin-Wang Tao; Wiley E. Thompson; Ramon Parra-Loera
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Paper Abstract

This paper presents a methodology for the classifying problem based upon a pseudo k-means algorithm. Both supervised and unsupervised classifying algorithms are presented here to show the flexibility of the pseudo k-means algorithm. The supervised algorithm is computationally efficient compared with the k-nn algorithm. The unsupervised algorithm avoids the error and time consuming problem due to the improper selection of initial class centers in the k-means algorithm. The pseudo k-means algorithm is easy to extend to the high dimension situation. Examples are presented to illustrate the effectiveness of the approach.

Paper Details

Date Published: 9 July 1992
PDF: 8 pages
Proc. SPIE 1699, Signal Processing, Sensor Fusion, and Target Recognition, (9 July 1992); doi: 10.1117/12.138225
Show Author Affiliations
Chin-Wang Tao, New Mexico State Univ. (United States)
Wiley E. Thompson, New Mexico State Univ. (United States)
Ramon Parra-Loera, New Mexico State Univ. (United States)


Published in SPIE Proceedings Vol. 1699:
Signal Processing, Sensor Fusion, and Target Recognition
Vibeke Libby; Ivan Kadar, Editor(s)

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