DocumentCode
1808624
Title
Input variable selection using independent component analysis
Author
Back, Andrew D. ; Trappenberg, Thomas P.
Author_Institution
RIKEN, Inst. of Phys. & Chem. Res., Saitama, Japan
Volume
2
fYear
1999
fDate
36342
Firstpage
989
Abstract
The problem of input variable selection is well known in the task of modeling real world data. In this paper we propose a novel model-free algorithm for input variable selection using independent component analysis and higher order cross statistics. Experimental results are given which indicate that the method is capable of giving reliable performance and that it outperforms other approaches when the inputs are dependent
Keywords
computational complexity; higher order statistics; learning (artificial intelligence); neural nets; principal component analysis; computational complexity; higher order cross statistics; independent component analysis; input variable selection; learning; Biomedical measurements; Chemicals; Context modeling; Cost function; Filters; Independent component analysis; Input variables; Optimization methods; Statistical analysis; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location
Washington, DC
ISSN
1098-7576
Print_ISBN
0-7803-5529-6
Type
conf
DOI
10.1109/IJCNN.1999.831089
Filename
831089
Link To Document