A new kernel fuzzy based feature extraction method using attraction points

A new kernel fuzzy based feature extraction method using attraction points 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 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multidimensional Systems and Signal Processing Springer Journals

A new kernel fuzzy based feature extraction method using attraction points

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Publisher
Springer US
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Engineering; Circuits and Systems; Electrical Engineering; Signal,Image and Speech Processing; Artificial Intelligence (incl. Robotics)
ISSN
0923-6082
eISSN
1573-0824
D.O.I.
10.1007/s11045-018-0592-2
Publisher site
See Article on Publisher Site

Abstract

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

Journal

Multidimensional Systems and Signal ProcessingSpringer Journals

Published: Jun 4, 2018

References

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