Centroid Neural Network with Bhattacharyya Kernel 


Vol. 32,  No. 9, pp. 861-866, Sep.  2007


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  Abstract

A clustering algorithm for Gaussian Probabili쇼 Distribution Function (GPDF) data called Centroid Neural Network with a Bhattacharyya Kernel (BK-CNN) is proposed in this paper. The proposed BK-CNN is based on the unsupervised competitive Centroid Neural Network (CNN) and employs a kernel method for data projection. The kernel method adopted in the proposed BK-CNN is used to project data from the low dimensional input feature space into higher dimensional feature space so as the nonlinear problems associated with input space can be solved linearly in the feature space. In order to cluster the GPDF data, the Bhattacharyya kernel is used to measure the distance between two probability distributions for data projection. With the incorporation of the kernel method, the proposed BK-CNN is capable of dealing with nonlinear separation boundaries and can successfully allocate more code vector in the region that GPDF data are densely distributed. When applied to GPDF data in an image classification probleml, the experiment results show that the proposed BK-CNN algorithm gives 1.7% - 4.3% improvements in average classification accuracy over other conventional algorithm such as k-means, Self-Organizing Map (SOM) and CNN algorithms with a Bhattacharyya distance, classed as Bk-Means, B-SOM, B-CNN algorithms.

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  Cite this article

[IEEE Style]

S. Lee and D. Park, "Centroid Neural Network with Bhattacharyya Kernel," The Journal of Korean Institute of Communications and Information Sciences, vol. 32, no. 9, pp. 861-866, 2007. DOI: .

[ACM Style]

Song-Jae Lee and Dong-Chul Park. 2007. Centroid Neural Network with Bhattacharyya Kernel. The Journal of Korean Institute of Communications and Information Sciences, 32, 9, (2007), 861-866. DOI: .

[KICS Style]

Song-Jae Lee and Dong-Chul Park, "Centroid Neural Network with Bhattacharyya Kernel," The Journal of Korean Institute of Communications and Information Sciences, vol. 32, no. 9, pp. 861-866, 9. 2007.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880