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Empirical Inference Article 2010

Remote Sensing Feature Selection by Kernel Dependence Estimation

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Empirical Inference
Bernhard Schölkopf
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Empirical Inference
Gustavo Camps-Valls

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.

Author(s): Camps-Valls, G. and Mooij, JM. and Schölkopf, B.
Journal: IEEE Geoscience and Remote Sensing Letters
Volume: 7
Number (issue): 3
Pages: 587-591
Year: 2010
Month: July
Day: 0
Bibtex Type: Article (article)
DOI: 10.1109/LGRS.2010.2041896
Digital: 0
Electronic Archiving: grant_archive
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik
Links:

BibTex 

@article{6263,
  title = {Remote Sensing Feature Selection by Kernel Dependence Estimation},
  journal = {IEEE Geoscience and Remote Sensing Letters},
  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.},
  volume = {7},
  number = {3},
  pages = {587-591},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = jul,
  year = {2010},
  slug = {6263},
  author = {Camps-Valls, G. and Mooij, JM. and Sch{\"o}lkopf, B.},
  month_numeric = {7}
}