Approximate non-parametric feature extraction applied to classification system data

Kathrin Dorn; Sabino Gadaleta; Can Altinigneli

doi:10.1117/12.830113

23 September 2009 Approximate non-parametric feature extraction applied to classification system data

Kathrin Dorn, Sabino Gadaleta, Can Altinigneli

Proceedings Volume 7481, Electro-Optical and Infrared Systems: Technology and Applications VI; 748104 (2009) https://doi.org/10.1117/12.830113
Event: SPIE Security + Defence, 2009, Berlin, Germany

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.

Citation Download Citation

Kathrin Dorn, Sabino Gadaleta, and Can Altinigneli "Approximate non-parametric feature extraction applied to classification system data", Proc. SPIE 7481, Electro-Optical and Infrared Systems: Technology and Applications VI, 748104 (23 September 2009); https://doi.org/10.1117/12.830113

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