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http://dx.doi.org/10.3745/KIPSTB.2004.11B.3.381

Independent Component Analysis of Fixed-Point Algorithm for Clustering Components Using Kurtosis  

Cho, Yong-Hyun (대구가톨릭대학교 컴퓨터ㆍ정보통신공학부)
Kim, A-Ram (대구가톨릭대학교 대학원 전산통계학과)
Publication Information
The KIPS Transactions:PartB / v.11B, no.3, 2004 , pp. 381-386 More about this Journal
Abstract
This paper proposes an independent component analysis(ICA) of the fixed-point(FP) algorithm based on Newton method by adding the kurtosis. The kurtosis is applied for clustering the components, and the FP algorithm of Newton method is applied for improving the analysis speed and performance. The proposed ICA has been applied to the problems for separating the 6-mixed signals of 500 samples and 8-mixed images of $512\times512$pixels, respectively. The experimental results show that the proposed ICA has always a fixed analysis sequence. The result can be solved the limit of conventional ICA which has a variable sequence depending on the running of algorithm. Especially, the proposed ICA can be used to classify and identify the signals or the images.
Keywords
Independent Component Analysis; Fixed-Point Algorithm; Kurtosis; Clustering; Blind Source Separation;
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