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14

IDEAL
2009
Springer

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Supervised Feature Extraction Using Hilbert-Schmidt Norms

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Supervised Feature Extraction Using Hilbert-Schmidt Norms

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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.

Povilas Daniusis, Pranas Vaitkus

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Data Mining | Data Sets Reveal | Feature Extraction Procedure | Hilbert-Schmidt Independence Criterion | IDEAL 2009 |

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Added	26 May 2010
Updated	26 May 2010
Type	Conference
Year	2009
Where	IDEAL
Authors	Povilas Daniusis, Pranas Vaitkus

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