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  Remote Sensing Feature Selection by Kernel Dependence Estimation

Camps-Valls, G., Mooij, J., & Schölkopf, B. (2010). Remote Sensing Feature Selection by Kernel Dependence Estimation. IEEE Geoscience and Remote Sensing Letters, 7(3), 587-591. doi:10.1109/LGRS.2010.2041896.

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Camps-Valls, G, Author           
Mooij, JM1, 2, Author           
Schölkopf, B1, 2, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: This letter introduces a nonlinear measure of independence
between random variables for remote sensing supervised
feature selection. The so-called Hilbert–Schmidt independence
criterion (HSIC) is a kernel method for evaluating statistical
dependence and it is based on computing the Hilbert–Schmidt
norm of the cross-covariance operator of mapped samples in the
corresponding Hilbert spaces. The HSIC empirical estimator is
easy to compute and has good theoretical and practical properties.
Rather than using this estimate for maximizing the dependence
between the selected features and the class labels, we propose
the more sensitive criterion of minimizing the associated HSIC
p-value. Results in multispectral, hyperspectral, and SAR data
feature selection for classification show the good performance of
the proposed approach.

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 Dates: 2010-07
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1109/LGRS.2010.2041896
BibTex Citekey: 6263
 Degree: -

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Title: IEEE Geoscience and Remote Sensing Letters
  Other : IEEE Geosci. Remote Sens. Lett.
Source Genre: Journal
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Publ. Info: Piscataway, NJ : Institute of Electrical and Electronics Engineers
Pages: - Volume / Issue: 7 (3) Sequence Number: - Start / End Page: 587 - 591 Identifier: ISSN: 1545-598X
CoNE: https://pure.mpg.de/cone/journals/resource/954925491886