Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Design of Pattern Classification Rule based on Local Linear Discriminant Analysis Classifier by using Differential Evolutionary Algorithm

차분진화 알고리즘을 이용한 지역 Linear Discriminant Analysis Classifier 기반 패턴 분류 규칙 설계

  • 노석범 (원광대학교 전자 및 제어 공학부) ;
  • 황은진 (원광대학교 전자 및 제어 공학부) ;
  • 안태천 (원광대학교 전자 및 제어 공학부)
  • Received : 2011.11.19
  • Accepted : 2012.02.11
  • Published : 2012.02.25
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Abstract

In this paper, we proposed a new design methodology of a pattern classification rule based on the local linear discriminant analysis expanded from the generic linear discriminant analysis which is used in the local area divided from the whole input space. There are two ways such as k-Means clustering method and the differential evolutionary algorithm to partition the whole input space into the several local areas. K-Means clustering method is the one of the unsupervised clustering methods and the differential evolutionary algorithm is the one of the optimization algorithms. In addition, the experimental application covers a comparative analysis including several previously commonly encountered methods.

본 논문에서는 전형적인 Linear Discriminant Analysis을 확장시켜 전체 입력공간을 다수의 지역공간으로 분할하고 분할된 공간에 Local Linear Discriminant Analysis 기반으로 하여 패턴 분류 규칙을 설계하는 새로운 방법을 제안한다. 전체 입력공간을 여러 개의 지역공간으로 분할하기 위한 방법으로 unsupervised clustering의 대표적인 방법인 k-Means 클러스터링 기법과 최적화 알고리즘인 차분 진화 연산 알고리즘을 사용한다. 제안된 알고리즘의 성능 평가를 위해 기존의 패턴 분류기와 비교 결과를 제시한다.

Keywords

References

  1. C.M. Bishop, Pattern Recognition and Machine Learning, Springer, Berlin, 2006.
  2. R. Q. Duda, P. E. Hart, D. G. Stork, Pattern Classification, second ed., Wiley, New York, 2001.
  3. K. Fukunaga, Introduction to Statistical Pattern Recognition, second ed., Academic Press, New York, 1990.
  4. G. Kowalski, Information Retrieval Systems: Theory and Implementation, Kluwer Academic Publishers, Dordrecht, 1997.
  5. W. B. Frakes, R. Baeza-Yates, Information Retrieval: Data Structures and Algorithms, Prentice-Hall PTR, 1992.
  6. Z. Jin, J. Y. Yang, Z. S. Hu, Z. Lou, "Face recognition based on the uncorrelated discriminant transformation," Pattern Recognition, vol. 34, pp. 1405-1416, 2001. https://doi.org/10.1016/S0031-3203(00)00084-4
  7. D. L. Swets, J. Weng, "Using discriminant eigenfeatures for image retrieval," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18 pp. 831-836, 1996. https://doi.org/10.1109/34.531802
  8. X. Li, W. Hu, H. Wang, and Z. Zhang, "Linear discriminant analysis using rotational invariant L1 Norm," Neurocomputing, pp. 2571-2579, 2010.
  9. K. Fukunaga, J.M. Mantock, "Nonparametric discriminant analysis," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 5, pp. 671-678, 1983. https://doi.org/10.1109/TPAMI.1983.4767461
  10. H. Brunzell, J. Eriksson, "Feature reduction for classification of multidimensional data," Pattern Recognition, vol. 33, pp. 1741-1748, 2000. https://doi.org/10.1016/S0031-3203(99)00142-9
  11. M. Sugiyama, "Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis," Journal of Machine Learning Research, vol. 8 pp. 1027-1061, 2007.
  12. R. Storn, K. V. Price, "Differential Evolution-a fast and efficient heuristic for global optimization over continuous spaces," Journal of Global Optimization, vol. 11, pp. 341-359, 1997. https://doi.org/10.1023/A:1008202821328
  13. Z. Michalewicz, "Genetic Algorithms + Data Structures = Evolution Programs," Springer-Verlag, Berlin Heidelberg, 1996.
  14. R. Parades, E. Vidal, "Learning prototypes and distance: A prototype reduction technique based on nearest neighbor error minimization," Pattern Recognition, vol. 39, pp. 180-188, 2006. https://doi.org/10.1016/j.patcog.2005.06.001
  15. J. H. Na, M. S. Park, and J. Y. Choi, "Linear boundary discriminant analysis," Pattern Recognition, vol. 43, pp. 929-936, 2010. https://doi.org/10.1016/j.patcog.2009.09.015
  16. A. Cervantes, I. M. Galvan, and P. Isasi, "AMPSO: A New Particle Swarm Method for Nearest Neighborhood Classification," IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS-PART B: CYBERNETICS, vol. 39, no. 5, pp. 1081-1091, 2009.
  17. 노석범, 안태천, "차분 진화 알고리즘을 이용한 Nearest Prototype Classifier 설계," 한국지능시스템학회 논문지, Vol. 21, No. 4, pp. 487-492, 2011.