Pseudo k-means approach to the classifying problem

Chin-Wang Tao; Wiley E. Thompson; Ramon Parra-Loera

doi:10.1117/12.138225

9 July 1992 Pseudo k-means approach to the classifying problem

Chin-Wang Tao, Wiley E. Thompson, Ramon Parra-Loera

Proceedings Volume 1699, Signal Processing, Sensor Fusion, and Target Recognition; (1992) https://doi.org/10.1117/12.138225
Event: Aerospace Sensing, 1992, Orlando, FL, United States

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.

Citation Download Citation

Chin-Wang Tao, Wiley E. Thompson, and Ramon Parra-Loera "Pseudo k-means approach to the classifying problem", Proc. SPIE 1699, Signal Processing, Sensor Fusion, and Target Recognition, (9 July 1992); https://doi.org/10.1117/12.138225

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