Hilbert–Schmidt component analysis
Articles
Povilas Daniušis
Vilnius University
Pranas Vaitkus
Vilnius University
Linas Petkevičius
Vilnius University
Published 2016-12-15
https://doi.org/10.15388/LMR.A.2016.02
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Keywords

feature extraction
dimensionality reduction
HSCA
Hilbert–Schmidt independence criterion
kernel methods

How to Cite

Daniušis P., Vaitkus P. and Petkevičius L. (2016) “Hilbert–Schmidt component analysis”, Lietuvos matematikos rinkinys, 57(A), pp. 7–11. doi: 10.15388/LMR.A.2016.02.

Abstract

We propose a feature extraction algorithm, based on the Hilbert–Schmidt independence criterion (HSIC) and the maximum dependence – minimum redundancy approach. Experiments with classification data sets demonstrate that suggested Hilbert–Schmidt component analysis (HSCA) algorithm in certain cases may be more efficient than other considered approaches.

 

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