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MRS Pattern Classification Using Fusion Method based on SpPCA and MLP  

Song Chang kyu (충북대학교 전기전자컴퓨터공학부)
Lee Dae jong (충북대학교 전기전자컴퓨터공학부)
Jeon Byeong seok ((주)세화폴리텍)
Ryu Jeong woong (충북대학교 전기전자컴퓨터공학부)
Publication Information
The Journal of Korean Institute of Communications and Information Sciences / v.30, no.9C, 2005 , pp. 922-929 More about this Journal
Abstract
In this paper, we propose the MRS p:Ittern classification techniques by the fusion scheme based on the SpPCA and MLP. A conventional PCA teclulique 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.
Keywords
PCA; SPPCA; MLP; MRS pattern classification;
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1 R. Baumgarttner, R. Somorjai, C. Bowman, T.C. Sorrell, C.E. Mountford, U. Himmelreich, Unsupervised feature dimension reduction for classification of MR spectra, Magnetic Resonance Imaging 22, pp. 251-256, 2004   DOI   ScienceOn
2 E.K. Tang, P.N. Suganthan, X. Yao and A.K. Qin, Linear dimensionality reduction using relevance weighted LDA, Pattern Recognition, Vol.38, Issue4, pp.485-493, 2005   DOI   ScienceOn
3 N.P. Hughes, S.J. Roberts, L. Tarassenko, Semi-supervised learning of probabilistic models for ECG segmentation, Engineering in Medicine and Biology Society, EMBC 2004, Vol.1, pp.434-437, 2004
4 Yang-Lang Chang, Chin-Chuan Han, Fan-Di Jou, Kuo-Chin Fan, K.S. Chen, Jeng-Horng Chang, A modular eigen subspace scheme for high-dimensional data classification, Future Generation Computer System 20, pp.1131-1143, 2004   DOI   ScienceOn
5 Y.Y.B. Lee, Y. Huang, W. El-Deredy, P.J.G. Lisboa, C. Arus, P. Harris, Robust methodology for the discrimination of brain tumours from in vivo magnetic resonance spectra: Science, Measurement and Technology, IEE Proc., Vol.147, Issue 6, pp.309-314, 2000   DOI   ScienceOn
6 Yu Wang, S. Van Huffel, E. Heyvaert, L. Vanhamme, N. Mastronardi, P. Van Hecke, Magnetic resonance spectroscopic quantitation via complex principal component analysis, WCCC-ICSP 2000, Vol.3, pp.2074-2077, 2000
7 Songcan Chen, Yulian Zhu, Subpattern- based principal component analysis, Pattern Recognition 37, pp.1081-1083, 2004   DOI   ScienceOn
8 Nicolino J. Pizzi, Fuzzy pre-processing of gold standards as applied to biomedical spectra classification, Artificial Intelligence in Medicine
9 Rajkiran Gottumukkal, Vijayan K. Asari, An improved face recognition technique based on modular PCA approach, Pattern Recognition Letters 25, pp.429-436, 2004   DOI   ScienceOn
10 Hong Yang, Joseph Irudayaraj and Manish M. Paradkar, Discriminant analysis of edible oils and fats by FTIR, FT-NIR and FT-Raman spectroscopy, Food Chemistry, Vol.93, Issue 1, pp.25-32, 2005   DOI   ScienceOn
11 M. Turk and A. Pentland, Face recognition using eigenfaces, IEEE Conf. on Computer Vision and Pattern Recognition, pp.586-591, 1991
12 Marina Vannucci, Naijun Sha and Philip J. Brown, NIR and mass spectra classification: Bayesian methods for wavelet-based feature selection, Chemometrics and Intelligent Laboratory Systems, In Press, Corrected Proof, Available online 4 March 2005