Independent Component Analysis for Clustering Analysis Components by Using Kurtosis 


Vol. 11,  No. 4, pp. 429-436, Aug.  2004
10.3745/KIPSTB.2004.11.4.429


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  Abstract

This paper proposes an independent component analyses(ICAs) of the fixed-point (FP) algorithm based on Newton and secant method by adding the kurtosis, respectively. The kurtosis is applied to cluster the analyzed components, and the FP algorithm is applied to get the fast analysis and superior performance irrelevant to learning parameters. The proposed ICAs have been applied to the problems for separating the 6-mixed signals of 500 samples and 10-mixed images of 512x512 pixels, respectively. The experimental results show that the proposed ICAs have always a fixed analysis sequence. The results can be solved the limit of conventional ICA without a kurtosis which has a variable sequence depending on the running of algorithm. Especially, the proposed ICA can be used for classifying and identifying the signals or the images. The results also show that the secant method has better the separation speed and performance than Newton method. And, the secant method gives relatively larger improvement degree as the problem size increases.

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[IEEE Style]

Y. H. Cho, "Independent Component Analysis for Clustering Analysis Components by Using Kurtosis," The KIPS Transactions:PartB , vol. 11, no. 4, pp. 429-436, 2004. DOI: 10.3745/KIPSTB.2004.11.4.429.

[ACM Style]

Yong Hyun Cho. 2004. Independent Component Analysis for Clustering Analysis Components by Using Kurtosis. The KIPS Transactions:PartB , 11, 4, (2004), 429-436. DOI: 10.3745/KIPSTB.2004.11.4.429.

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