PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Remote Sensing Feature Selection by Kernel Dependence Measures
Gustavo Camps-Valls, Joris Mooij and Bernhard Schölkopf
IEEE Geoscience and Remote Sensing Letters Volume 7, Number 3, pp. 587-591, 2010. ISSN 1545-598X

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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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:6383
Deposited By:Joris Mooij
Deposited On:26 February 2010