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
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;
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-5529-6
DOI :
10.1109/IJCNN.1999.831089
