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.
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© 2005 Springer-Verlag Berlin Heidelberg
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Kuo, BC., Li, CH. (2005). Kernel Nonparametric Weighted Feature Extraction for Classification. In: Zhang, S., Jarvis, R. (eds) AI 2005: Advances in Artificial Intelligence. AI 2005. Lecture Notes in Computer Science(), vol 3809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11589990_59
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DOI: https://doi.org/10.1007/11589990_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30462-3
Online ISBN: 978-3-540-31652-7
eBook Packages: Computer ScienceComputer Science (R0)