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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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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Trappenberg, T.P.
description 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.
doi_str_mv 10.1109/IJCNN.1999.831089
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source IEEE Electronic Library (IEL) Conference Proceedings
subjects Biomedical measurements
Chemicals
Context modeling
Cost function
Filters
Independent component analysis
Input variables
Optimization methods
Statistical analysis
Testing
title Input variable selection using independent component analysis
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