Enhanced Kernel Uncorrelated Discriminant Nearest Feature Line Analysis for Radar Target Recognition

Chunyu Wan, Xuelian Yu, Yun Zhou, Xuegang Wang

2014

Abstract

In this paper, a new subspace learning algorithm, called enhanced kernel uncorrelated discriminant nearest feature line analysis (EKUDNFLA), is presented. The aim of EKUDNFLA is to seek a feature subspace in which the within-class feature line (FL) distances are minimized and the between-class FL distances are maximized simultaneously. At the same time, an uncorrelated constraint is imposed to get statistically uncorrelated features, which contain minimum redundancy and ensure independence, and thus it is highly desirable in many practical applications. Optimizing an objective function in a kernel feature space, nonlinear features are extracted. In addition, a weighting coefficient is introduced to adjust the proportion between within-class and between-class information to get an optimal effect. Experimental results on radar target recognition with measured data demonstrate the effectiveness of the proposed method.

References

  1. Chen, B., Liu, H.W., Bao, Z., 2005. PCA and kernel PCA for radar high range resolution profiles recognition. In the Proc. of 2005 IEEE International Radar Conference. pp. 528-533.
  2. Yu, X.L., Liu, B.Y., 2008. Optimal kernel discriminant analysis for radar target recognition. In J. University of Electronic Science and Technology of China, vol.37, no.6, pp.883-885.
  3. Turk, M., Pentland, A., 1991. Eigenfaces for recognition. In J. Cogn. Neurosci.,vol. 3, no. 1, pp. 71-86.
  4. Belhumenur, P.N., Hepanha, J.P., Kriegman, D.J.,1997. Eigenfaces vs. Fisherface: Recognition using class specific linear projection. In IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no. 7, pp. 711-720.
  5. He, X., Yan, S., Hu, Y., Niyogi, P., Zhang, H.J., 2005. Face recognition using Laplacianface. In IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 3, pp. 328-340.
  6. Pang, Y.W., Zheng, L., Liu, Z.K., Yu, N.H., Li, H.Q., 2005. A novel linear dimension reduction method. In Proc. ICIC, pp. 117-125.
  7. He, X.F., Cai, D., Yan, S.C., et al., 2005. Neighborhood preserving embedding. In Proe. 10th IEEE Int. Conf. ComputerVision, pp. 1208-1213.
  8. Li, S., Lu, J.,1999. Face recognition using the nearest feature line method. In IEEE Trans. Neural Networks, vol. 10, no. 2, pp. 439-443.
  9. Pang, Y., Yuan, Y., Li, X., 2007. Generalized nearest feature line for subspace learning. In Electron. Lett., vol. 43, no. 20, pp. 1079-1080.
  10. Zheng, Y.J., Yang, J.Y., Yang, J, Wu, X.J., Jin, Z., 2006. Nearest neighbour line nonparametric discriminant analysis for feature extraction. In Electron. Lett., vol. 42, no. 12, pp. 679-680.
  11. Lu, J., Tan, Y.P., 2010. Uncorrelated discriminant nearest feature line analysis for face recognition. In IEEE Signal Process. Lett., vol. 17, no.2, pp. 185-188.
  12. Yan, L., Pan, J.S., 2011. Neighborhood discriminant nearest feature line analysis for face recognition. In 2nd Int. Conf. Innovations, pp. 345-348.
  13. Yu, X.L., Wang, X.G., 2008. Uncorrelated discriminant locality preserving projections. In IEEE Signal Process. Lett., vol. 15, pp. 361-364.
  14. Scholkopf, B., Smola, A., Muller, K.R., 1998. Nonlinearcomponent analysis as a kernel eigenvalue problem. In Neural Computation, vol. 10, no. 5, pp. 1299-1319.
  15. Mika, S., Ratsch, G., Weston, J., et al., 1999. Fisher discriminant analysis with kernels. In Proc IEEE Int. Workshop on Neural Networks for Signal Processing .Madison:Wisconsin, pp. 41-48.
Download


Paper Citation


in Harvard Style

Wan C., Yu X., Zhou Y. and Wang X. (2014). Enhanced Kernel Uncorrelated Discriminant Nearest Feature Line Analysis for Radar Target Recognition . In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-018-5, pages 155-160. DOI: 10.5220/0004759701550160


in Bibtex Style

@conference{icpram14,
author={Chunyu Wan and Xuelian Yu and Yun Zhou and Xuegang Wang},
title={Enhanced Kernel Uncorrelated Discriminant Nearest Feature Line Analysis for Radar Target Recognition},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2014},
pages={155-160},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004759701550160},
isbn={978-989-758-018-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Enhanced Kernel Uncorrelated Discriminant Nearest Feature Line Analysis for Radar Target Recognition
SN - 978-989-758-018-5
AU - Wan C.
AU - Yu X.
AU - Zhou Y.
AU - Wang X.
PY - 2014
SP - 155
EP - 160
DO - 10.5220/0004759701550160