Supervised Feature Extraction Using Hilbert-Schmidt Norms

@inproceedings{Daniusis2009SupervisedFE,
  title={Supervised Feature Extraction Using Hilbert-Schmidt Norms},
  author={Povilas Daniusis and Pranas Vaitkus},
  booktitle={IDEAL},
  year={2009}
}
  • Povilas Daniusis, Pranas Vaitkus
  • Published in IDEAL 2009
  • Computer Science
  • 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. 

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