Supervised kernel neighborhood preserving projections for radar target recognition

Signal Processing

Volumn 88, Issue 9, 2008, Pages 2335-2339

  Yu, Xuelian ^a   Wang, Xuegang ^a   Liu, Benyong ^b  

^a UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

^b GUIZHOU UNIVERSITY   (China)

Author keywords

Kernel technique; Manifold learning; Neighborhood preserving projections; Nonlinear dimensionality reduction; Radar target recognition

Indexed keywords

ALGORITHMS; NONLINEAR ANALYSIS;

KERNEL TECHNIQUE; MANIFOLD LEARNING; NEIGHBORHOOD PRESERVING PROJECTIONS; NONLINEAR DIMENSIONALITY REDUCTION;

RADAR TARGET RECOGNITION;

EID: 44649127326     PISSN: 01651684     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.sigpro.2007.11.015     Document Type: Article

References (16)

1. Tenenbaum J.B., Vin de Silva S., and Langford J.C. A global geometric framework for nonlinear dimensionality reduction. Science 290 5500 (2000) 2319-2323
2. Roweis S.T., and Saul L.K. Nonlinear dimensionality reduction by locally linear embedding. Science 290 5500 (2000) 2323-2326
3. Saul L.K., and Roweis S.T. Think globally, fit locally: unsupervised learning of low dimensional manifolds. J. Mach. Learn. Res. 4 (2003) 119-155
4. M. Belkin, P. Niyogi, Laplacian Eigenmaps and spectral techniques for embedding and clustering, Advances in Neural Information Processing Systems, vol. 14, MIT Press, Cambridge, MA, 2002, pp. 585-591.
5. Belkin M., and Niyogi P. Laplacian Eigenmaps for dimensionality reduction and data representation. Neural Comput. 5 6 (2003) 1373-1396
6. Zhang Z., and Zha H. Principal manifolds and nonlinear dimensionality reduction via tangent space alignment. SIAM J. Sci. Comput. 26 1 (2004) 313-338
7. Q.S. Liu, R. Huang, H.Q. Lu, S.D. Ma, Face recognition using kernel based fisher discriminant analysis, in: Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition, Washington, 2002, pp. 197-201.
8. X.F. He, P. Niyogi, Locality preserving projections, Advances in Neural Information Processing Systems, vol. 16, MIT Press, Cambridge, MA, 2003, pp. 153-160.
9. X.F. He, S.C. Yan, Y.X. Hu, H.J. Zhang, Learning locality preserving subspace for visual recognition, in: Proceedings of the Ninth IEEE International Conference on Computer Vision, 2003, pp. 385-392.
10. He X.F., Yan S.C., Hu Y.X., Niyogi P., and Zhang H.J. Face recognition using Laplacian faces. IEEE Trans. Pattern Anal. Mach. Intell. 27 3 (2005) 328-340
11. Y.W. Pang, L. Zheng, Z.K. Liu, N.H. Yu, H.Q. Li, Neighborhood preserving projections (NPP): a novel linear dimension reduction method, in: D.S. Huang, X.-P. Zhang, G.-B. Huang (Eds.), ICIC 2005, Part I, Lecture Notes in Computer Science, vol. 3644, Springer, Berlin, 2005, pp. 117-125.
12. Y.W. Pang, N.H. Yu, H.Q. Li, R. Zhang, Z.K. Liu, Face recognition using neighborhood preserving projections, in:, Y.-S. Ho, H.J. Kim, (Eds.),, PCM 2005, Part II, Lecture Notes in Computer Science, vol. 3768, 2005, Springer, pp. 854, 864
13. X.F. He, D. Cai, S.C. Yan, H.J. Zhang, Neighborhood preserving embedding, in: Proceedings of the 10th IEEE International Conference on Computer Vision, 2005, pp. 1208-1213.
14. Belhumeur P.N., Hespanha J.P., and Kriegman D.J. Eigenfaces vs. Fisherfances: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19 7 (1997) 711-720
15. Zhang J., Liu B.Y., and Tan H. A kernel-based nonlinear representor with application to eigenface classification. J. Electron. Sci. Technol. China 2 2 (2004) 19-22
16. X.L. Yu, B.Y. Liu, X.G. Wang, High resolution range profile recognition using kernel-based nonlinear classifiers, in: Proceedings of the Seventh International Conference on Intelligent Technologies, 2006, pp. 116-120.