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http://dx.doi.org/10.5391/JKIIS.2015.25.4.355

Design of PCA-based pRBFNNs Pattern Classifier for Digit Recognition  

Lee, Seung-Cheol (Department of Electrical Engineering, The University of Suwon)
Oh, Sung-Kwun (Department of Electrical Engineering, The University of Suwon)
Kim, Hyun-Ki (Department of Electrical Engineering, The University of Suwon)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.25, no.4, 2015 , pp. 355-360 More about this Journal
Abstract
In this paper, we propose the design of Radial Basis Function Neural Network based on PCA in order to recognize handwritten digits. The proposed pattern classifier consists of the preprocessing step of PCA and the pattern classification step of pRBFNNs. In the preprocessing step, Feature data is obtained through preprocessing step of PCA for minimizing the information loss of given data and then this data is used as input data to pRBFNNs. The hidden layer of the proposed classifier is built up by Fuzzy C-Means(FCM) clustering algorithm and the connection weights are defined as linear polynomial function. In the output layer, polynomial parameters are obtained by using Least Square Estimation (LSE). MNIST database known as one of the benchmark handwritten dataset is applied for the performance evaluation of the proposed classifier. The experimental results of the proposed system are compared with other existing classifiers.
Keywords
PCA; pRBFNNs; Fuzzy C-Means Clustering; Least Square Estimation;
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1 S. K. Oh, W. Pedrycz, B. J. Park, "Polynomial-bas ed Radial Basis Function Neural Networks realized with the Aid of Particle Swarm Optimization," Fuzzy Sets and Systems, Vol. 163, pp. 54-77, 2011.   DOI
2 S. B. Roh, S. C. Joo, W. Pedrycz, and S. K. Oh, "The development of fuzzy radial basis function neural networks based on the concept of information ambiguity," Neurocomputing, Vol. 73, No.13-15, pp. 2464-2477. 2010.   DOI
3 W. Pedrycz, "Conditional fuzzy clustering in the design of radial basis function neural networks", IEEE Trans. Neural Networks, vol.9, pp.601-612, July 1998.   DOI
4 J. C. Bezdek, Pattern recognition with Fuzzy Objective Function Algorithm, Plenum, New York, 1981.
5 S. P. Lloyd, "Least squares quantization in PCM," IEEE Tran. on Information Theory, vol. 28, no. 2, pp. 129-137, 1992.   DOI
6 H. Addi and L. J. Williams, "Principal component analysis," Wiley Interdisciplinary Reviews: Computational Ststicstics, vol. 2, no. 4, pp. 433-459, 2010.   DOI
7 Y. Ke, and R. Sukthankar, "PCA-SIFT: A More Distinctive Representation for Local Image Descriptors", IEEE Computer Society Conference on Computer Vision and Pattern Recognition Vol. 2, 506-513. 2004.
8 S. Knerr and L. Personnaz and G. Dreyfus, "Handwritten digit recognition by neural networks with single-layer training," IEEE Trans. Neural Networks, Vol. 3, No. 6, pp. 962-968, 1992.   DOI
9 S. W. Lee, "Off-line recognition of totally unconstrained handwritten numerals using multi layer cluster neural network," IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.18, No. 6, pp.648-652, 1996.   DOI
10 Y. LeCun, L. Bottou, Y. Bengio and P. Haffner "Gradient-Based Learning Applied to Document Recognition", IEEE, Vol. 86, pp. 2278-2324, 1998.   DOI