Supervised feature extraction using Hilbert-Schmidt norms

Authors:
P. Daniušis;P. Vaitkus
Affiliations:
Vilnius University, Vilnius, Lithuania and Vilnius Management Academy, Vilnius, Lithuania;Vilnius University, Vilnius, Lithuania
Venue:
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Year:
2009

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Abstract

We propose a novel, supervised feature extraction procedure, based on an unbiased estimator of the Hilbert-Schmidt independence criterion (HSIC). The proposed procedure can be directly applied to single-label or multi-label data, also the kernelized version can be applied to any data type, on which a positive definite kernel function has been defined. Computer experiments with various classification data sets reveal that our approach can be applied more efficiently than the alternative ones.