Input variable selection using independent component analysis

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...

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Bibliographic Details
Main Authors: Back, A.D., Trappenberg, T.P.
Format: Conference Proceeding
Language:English
Subjects:
Biomedical measurements
Chemicals
Context modeling
Cost function
Filters
Independent component analysis
Input variables
Optimization methods
Statistical analysis
Testing
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Description
Summary: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.
ISSN:1098-7576
1558-3902
DOI:10.1109/IJCNN.1999.831089