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http://dx.doi.org/10.5391/JKIIS.2006.16.5.525

Input Variable Selection by Using Fixed-Point ICA and Adaptive Partition Mutual Information Estimation  

Cho, Yong-Hyun (대구가톨릭대학교 컴퓨터정보통신공학부)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.16, no.5, 2006 , pp. 525-530 More about this Journal
Abstract
This paper presents an efficient input variable selection method using both fixed-point independent component analysis(FP-ICA) and adaptive partition mutual information(AP-MI) estimation. FP-ICA which is based on secant method, is applied to quickly find the independence between input variables. AP-MI estimation is also applied to estimate an accurate dependence information by equally partitioning the samples of input variable for calculating the probability density function(PDF). The proposed method has been applied to 2 problems for selecting the input variables, which are the 7 artificial signals of 500 samples and the 24 environmental pollution signals of 55 samples, respectively The experimental results show that the proposed methods has a fast and accurate selection performance. The proposed method has also respectively better performance than AP-MI estimation without the FP-ICA and regular partition MI estimation.
Keywords
Input Variable Selection; Fixed-Point Algorithm; Independent Component Analysis Mutual Information; Secant Method; Adaptive Partition;
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