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http://dx.doi.org/10.5391/JKIIS.2009.19.3.297

Feature Extraction and Classification of High Dimensional Biomedical Spectral Data  

Cho, Jae-Hoon (충북대학교 전기전자컴퓨터공학부)
Park, Jin-Il (충북대학교 전기전자컴퓨터공학부)
Lee, Dae-Jong (충북대학교 전기전자컴퓨터공학부)
Chun, Myung-Geun (충북대학교 전기전자컴퓨터공학부)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.19, no.3, 2009 , pp. 297-303 More about this Journal
Abstract
In this paper, we propose the biomedical spectral pattern classification techniques by the fusion scheme based on the SpPCA and MLP in extended feature space. A conventional PCA technique for the dimension reduction has the problem that it can't find an optimal transformation matrix if the property of input data is nonlinear. To overcome this drawback, we extract features by the SpPCA technique in extended space which use the local patterns rather than whole patterns. In the classification step, individual classifier based on MLP calculates the similarity of each class for local features. Finally, biomedical spectral 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.
Keywords
PCA; SpPCA; MLP; MRS pattern classification;
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