MRS Pattern Classification Using Fusion Method based on SpPCA and MLP 


Vol. 30,  No. 9, pp. 922-929, Sep.  2005


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  Abstract

In this paper, we propose the MRS pattern classification techniques by the fusion scheme based on the SpPCA and MLP. A conventional PCA technique for the dimension reduction has the problem that it can't find a optimal transformation matrix if the property of input data is nonlinear. To overcome this drawback, we extract features by the SpPCA technique which use the local patterns rather than whole patterns. In a next classification step, individual classifier based on MLP calculates the similarity of each class for local features. Finally, MRS patterns is classified by the fusion scheme to effectively combine the individual information. As the simulation results to verify the effectiveness, the proposed method showed more improved classification results than conventional methods.

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  Cite this article

[IEEE Style]

C. k. Song, D. j. Lee, B. s. Jeon, J. w. Ryu, "MRS Pattern Classification Using Fusion Method based on SpPCA and MLP," The Journal of Korean Institute of Communications and Information Sciences, vol. 30, no. 9, pp. 922-929, 2005. DOI: .

[ACM Style]

Chang kyu Song, Dae jong Lee, Byeong seok Jeon, and Jeong woong Ryu. 2005. MRS Pattern Classification Using Fusion Method based on SpPCA and MLP. The Journal of Korean Institute of Communications and Information Sciences, 30, 9, (2005), 922-929. DOI: .

[KICS Style]

Chang kyu Song, Dae jong Lee, Byeong seok Jeon, Jeong woong Ryu, "MRS Pattern Classification Using Fusion Method based on SpPCA and MLP," The Journal of Korean Institute of Communications and Information Sciences, vol. 30, no. 9, pp. 922-929, 9. 2005.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880