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Proceedings Paper

Approximate non-parametric feature extraction applied to classification system data
Author(s): Kathrin Dorn; Sabino Gadaleta; Can Altinigneli
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Paper Abstract

Within a Classification System (CS), prior to classification, feature extraction techniques are used to reduce the dimensionality of features. A standard unsupervised technique is the Principal Component Analysis (PCA). In this paper we apply a supervised method for feature extraction, the Non-Parametric eigenvalue-based Feature Extraction (NPFE), to CS data sets and compare the performance of the two different feature extraction schemes based on classification accuracies obtained with the LVQ cluster algorithm. Furthermore, to reduce computational complexity of NFPE, we introduce an approximate NFPE and show that it provides significantly reduced computation time with almost identical performance as in the full NPFE algorithm.

Paper Details

Date Published: 23 September 2009
PDF: 11 pages
Proc. SPIE 7481, Electro-Optical and Infrared Systems: Technology and Applications VI, 748104 (23 September 2009); doi: 10.1117/12.830113
Show Author Affiliations
Kathrin Dorn, EADS Deutschland GmbH (Germany)
Sabino Gadaleta, EADS Deutschland GmbH (Germany)
Can Altinigneli, EADS Deutschland GmbH (Germany)

Published in SPIE Proceedings Vol. 7481:
Electro-Optical and Infrared Systems: Technology and Applications VI
David A. Huckridge; Reinhard R. Ebert, Editor(s)

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