Hybrid Pattern Recognition Using a Combination of Different Features



Choi, Sang-Il

  • 투고 : 2015.08.21
  • 심사 : 2015.10.13
  • 발행 : 2015.11.30


We propose a hybrid pattern recognition method that effectively combines two different features for improving data classification. We first extract the PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) features, both of which are widely used in pattern recognition, to construct a set of basic features, and then evaluate the separability of each basic feature. According to the results of evaluation, we select only the basic features that contain a large amount of discriminative information for construction of the combined features. The experimental results for the various data sets in the UCI machine learning repository show that using the proposed combined features give better recognition rates than when solely using the PCA or LDA features.


Pattern classification;Feature extraction;Feature selection;Hybrid method;Discriminant analysis;Combined features


  1. S.-I. Choi, J. Oh, C.-H. Choi, and C. Kim, "Input variable selection for feature extraction in classification problems," Signal Processing, Vol. 92, No. 3, pp. 636-648, March 2011.
  2. S.T. Jung, "Robust Extraction of Facial Features under Illumination Variations," Journal of the Korea Society of Computer and Information, Vol. 10, No. 6, pp. 1-8, December 2005.
  3. C. Kim and C.-H. Choi, "A discriminant analysis using composite features for classification problems," Pattern Recognition, Vol. 40, No. 7, pp. 2118-2125, November 2007. https://doi.org/10.1016/j.patcog.2006.11.020
  4. G.Y. Heo, H. Choi, and J.S. Youn, "Supervised Rank Normalization with Training Sample Selection," Journal of the Korea Society of Computer and Information, Vol. 20, No. 1, pp. 21-28, January 2015.
  5. N. Kwak and C.-H. Choi, "Input feature selection by mutual information based on parzen window," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 12, pp. 1667-1671, December 2002. https://doi.org/10.1109/TPAMI.2002.1114861
  6. S.-I. Choi, C.-H. Choi, G.-M. Jeong, and N. Kwak, "Pixel selection based on discriminant features with application to face recognition," Pattern Recognition Letters, Vol. 33, No. 9, pp. 1083-1092, July 2012. https://doi.org/10.1016/j.patrec.2012.01.005
  7. J. Liang, S. Yang, and A. Winstanley, "Invariant optimal feature selection: a distance discriminant and feature ranking based solution," Pattern Recognition, Vol. 41, No. 5, pp. 1429-1439, May 2008. https://doi.org/10.1016/j.patcog.2007.10.018
  8. I. Kononenko, E. Simec, and M Robnik-Sikonja, "Overcoming the myopia of inductive learning algorithms with RELIEFF," Applied Intelligence , Vol. 7, No. 1, pp. 39-55, January 1997. https://doi.org/10.1023/A:1008280620621
  9. X. He, D. Cai, and P. Niyogi, "Laplacian score for feature selection," Advances in Neural Information Processing Systems, pp. 507-514, 2005.
  10. G.Y. Heo, C.S. Park, and C.W. Lee, "Training Sample and Feature Selection Methods for Pseudo Sample Neural Networks," Journal of the Korea Society of Computer and Information, Vol. 18, No. 4, pp. 19-26, March 2013.
  11. M. Turk, and A. Pentland, "Eigenfaces for recognition," Journal of Cognitive Neuroscience, Vol. 3, No. 1, pp. 71-86, 1991. https://doi.org/10.1162/jocn.1991.3.1.71
  12. P.N. Belhumeur, J.P. Hespanha, and D. J. Kriegman, "Eigenfaces vs. fisherfaces: recognition using class specific linear projection," IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, pp. 711-720, July 1997. https://doi.org/10.1109/34.598228
  13. C. Kim, S.-I. Choi, M. Turk, and C.-H. Choi, "A New Biased Discriminant Analysis Using Composite Vectors for Eye Detection," IEEE Trans. on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol. 42 No. 4, pp. 1095-1106, August 2012. https://doi.org/10.1109/TSMCB.2012.2186798
  14. J. Yang, D. Zhang, A.F. Frangi, and J.Y. Yang, "Two-dimensional PCA: a new approach to appearance-based face representation and recognition," IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 26, No. 1, pp. 131-137, January 2004. https://doi.org/10.1109/TPAMI.2004.1261097
  15. H. Cevikalp, M. Neamtu, M. Wilkes, and A. Barkana, "Discriminative common vectors for face recognition," IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 27, No. 1, pp. 4-13, January 2005. https://doi.org/10.1109/TPAMI.2005.9
  16. H. Yu and J. Yang, "A direct LDA algorithm for high-dimensional data - with application to face recognition," Pattern Recognition, Vol. 34, No. 10, pp. 2067-2070, October 2001. https://doi.org/10.1016/S0031-3203(00)00162-X
  17. J. Doak, "An evaluation of feature selection methods and their application to computer security," CSE Technical Report 92-18, University of California, Davis, 1992.
  18. N. Kwak, and C.-H. Choi, "Input feature selection for classification problems," IEEE Trans. on Neural Networks, Vol. 13, No. 1, pp. 143-159, January 2002. https://doi.org/10.1109/72.977291
  19. S.-I. Choi, "Face Recognition Based on 2D Images Under Various Conditions," Ph.D. Thesis, Seoul National University, 2010.
  20. N. Kwak, "Feature selection and extraction based on mutual information for classification," Ph.D. Thesis, Seoul National University, 2003.
  21. N. Kwak, and J. Oh, "Feature extraction for one-class classification problems: Enhancements to biased discriminant analysis," Pattern Recognition, Vol. 42, No. 1, pp. 17-26, January 2009. https://doi.org/10.1016/j.patcog.2008.07.002
  22. A.M. Martinez, and A.C. Kak, "PCA versus LDA," Pattern Recognition, Vol. 23, No. 2, pp. 228-233, February 2001.
  23. C. Kim, J. Oh, and C.-H. Choi, "Combined subspace method using global and local features for face recognition," in Proc. of International Joint Conference on Neural Networks, pp. 2030-2035, 2005.
  24. J. Ravi, and K.B. Raja, "Hybrid domain based face recognition system," International Journal of Advanced Networking and Applications, Vol. 3, No. 6, pp. 1402-1408, 2012.
  25. J. Kittler, M. Hatef, R.P. Duin, and J. Matas, "On combining classifiers," IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 20, No. 3, pp. 226-239, March 1998. https://doi.org/10.1109/34.667881
  26. Repository of Machine Learning Databases, http://www.ics.uci.edu/mlearn/MLRepository.html
  27. M. R. Sikonja and I. Kononenko, "Theoretical and Empirical Analysis of ReliefF and RReliefF," Machine Learining, Vol. 53, No. 1, pp. 23-69, October 2003. https://doi.org/10.1023/A:1025667309714
  28. S.-I. Choi, and G.-M. Jeong, "A discriminant distance based composite vector selection method for odor classification," Sensors, Vol. 14, No. 4, pp. 6938-6951, 2014. https://doi.org/10.3390/s140406938
  29. T.M. Cover, "Geometrical and Statistical properties of systems of linear inequalities with applications in pattern recognition," IEEE Trans. on Electronic Computers, Vol. EC-14, No. 3, pp. 326-334, June 1965. https://doi.org/10.1109/PGEC.1965.264137


연구 과제 주관 기관 : IITP(Institute for Information & communications Technology Promotion), National Research Foundation of Korea (NRF)