| Title |
Hilbert-Schmidt component analysis / |
| Translation of Title |
Hilberto-Šmito komponenčių analizė. |
| Authors |
Daniušis, Povilas ; Vaitkus, Pranas ; Petkevičius, Linas |
| DOI |
10.15388/LMR.A.2016.02 |
| Full Text |
|
| Is Part of |
Lietuvos matematikos rinkinys. Proceedings of the Lithuanian Mathematical Society. Ser. A.. Vilnius : Vilniaus universitetas. Matematikos ir informatikos institutas. 2016, t. 57, p. 7-11.. ISSN 0132-2818. eISSN 2335-898X |
| Keywords [eng] |
feature extraction ; dimensionality reduction ; HSCA ; Hilbert–Schmidt independence criterion ; kernel methods |
| Abstract [eng] |
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. |
| Published |
Vilnius : Vilniaus universitetas. Matematikos ir informatikos institutas |
| Type |
Journal article |
| Language |
English |
| Publication date |
2016 |
| CC license |
|
