Kernel nonparametric weighted feature extraction for classification

Authors:
Bor-Chen Kuo;Cheng-Hsuan Li
Affiliations:
Graduate School of Educational Measurement and Statistics, National Taichung University, Taichung, Taiwan, R.O.C;Department of Applied Math., National Chung Hsing University, Taichung, Taiwan, R.O.C
Venue:
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Year:
2005

Citing 3
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Abstract

Usually feature extraction is applied for dimension reduction in hyperspectral data classification problems. Many researches show that nonparametric weighted feature extraction (NWFE) is a powerful tool for extracting hyperspectral image features and kernel-based methods are computationally efficient, robust and stable for pattern analysis. In this paper, a kernel-based NWFE is proposed and a real data experiment is conducted for evaluating its performance. The experimental result shows that the proposed method outperforms original NWFE when the size training samples is large enough.