Multidim Syst Sign Process https://doi.org/10.1007/s11045-018-0592-2 A new kernel fuzzy based feature extraction method using attraction points 1 1 Hamid Reza Shahdoosti · Nayereh Javaheri Received: 22 April 2017 / Revised: 10 April 2018 / Accepted: 24 May 2018 © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract This paper aims at introducing a novel supervised feature extraction method to be used in small sample size situations. The proposed approach considers the class mem- bership of samples and exploits a nonlinear mapping in order to extract the relevant features and to mitigate the Hughes phenomenon. The proposed objective function is composed of three different terms, namely, attraction function, repulsion function, and the between-feature scatter matrix, where the last term increases the difference between extracted features. Subse- quently, the attraction function and the repulsion function are redeﬁned by incorporating the membership degrees of samples. Finally, the proposed method is extended using the kernel trick to capture the inherent nonlinearity of the original data. To evaluate the accuracy of the proposed feature extraction method, four remote sensing images are used in our experiments. The experiments indicate that the proposed feature extraction method is anappropriate choice for classiﬁcation of hyperspectral images
Multidimensional Systems and Signal Processing – Springer Journals
Published: Jun 4, 2018
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