Kernel class-wise locality preserving projection

Information Sciences

Volumn 178, Issue 7, 2008, Pages 1825-1835

  Li, Jun Bao ^a   Pan, Jeng Shyang ^b   Chu, Shu Chuan ^c  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

^b NATIONAL KAOHSIUNG UNIVERSITY OF APPLIED SCIENCES   (Taiwan)

^c CHENG SHIU UNIVERSITY   (Taiwan)

Author keywords

Class wise locality preserving projection; Feature extraction; Kernel method; Locality preserving projection; Manifold learning

Indexed keywords

ALGORITHMS; CLASSIFICATION (OF INFORMATION); DATA REDUCTION; DATABASE SYSTEMS; INFORMATION RETRIEVAL; LEARNING SYSTEMS;

CLASS-WISE LOCALITY PRESERVING PROJECTION; KERNEL METHOD; LOCALITY PRESERVING PROJECTION; MANIFOLD LEARNING;

PATTERN RECOGNITION;

EID: 38649136929     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2007.12.001     Document Type: Article

References (27)

1. Belhumeur P.N., Hespanha J.P., and Kriegman D.J. Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19 7 (1997) 711-720
2. A.U. Batur, M.H. Hayes, Linear subspace for illumination robust face recognition, in: Proc. IEEE Intl. Conf. Computer Vision and Pattern Recognition, 2001, pp. 296-301.
3. Chang K.-Y., and Ghosh J. A unified model for probabilistic principal surfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence 23 1 (2001) 22-41
4. Cho M.-y. A new gesture recognition algorithm and segmentation method of Korean scripts for gesture-allowed ink editor. Information Sciences 176 (2006) 1290-1303
5. Golub G.H., and Loan C.F.V. Matrix Computations. third ed. (1996), The Johns Hopkins University Press
6. Hastie T., and Stuetzle W. Principal curves. Journal of the American Statistical Association 84 (1989) 502-516
7. Hu Y.-C. Fuzzy integral-based perceptron for two-class pattern classification problems. Information Sciences 177 7 (2007) 1673-1686
8. X. He, P. Niyogi, Locality preserving projections, in: Proc. Conf. Advances in Neural Information Processing Systems, 2003, pp. 585-591.
9. He X., Yan S., Hu Y., Niyogi P., and Zhang H. Face recognition using Laplacianfaces. IEEE Transaction on Pattern Analysis and Machine Intelligence 27 3 (2005) 328-340
10. Jin B., Tang Y.C., and Zhang Y.-Q. Support vector machines with genetic fuzzy feature transformation for biomedical data classification. Information Sciences 177 2 (2007) 476-489
11. Liu C. Gabor-based kernel PCA with fractional power polynomial models for face recognition. IEEE Transaction on Pattern Analysis and Machine Intelligence 26 5 (2004) 572-581
12. Li J.-B., Chu S.-C., Pan J.-S., and Ho J.-H. Adaptive data-dependent matrix norm based Gaussian kernel for facial feature extraction. International Journal on Innovative Computing, Information and Control 3 5 (2007) 1263-1272
13. Mulier F., and Cherkassky V. Self-organization as an iterative kernel smoothing process. Neural Computation 7 (1995) 1165-1177
14. S. Mika, G. Ratsch, J. Weston, B. Scholkopf, K.-R. Müller, Fisher discriminant analysis with kernels, in: Proc. IEEE Intl. Workshop on Neural Networks for Signal Processing, vol. IX, 1999, pp. 41-48.
15. Müller K.R., Mika S., Rätsch G., Tsuda K., and Schölkopf B. An introduction to kernel-based learning algorithms. IEEE Transaction on Neural Networks 12 (2001) 181-201
16. Ritter H., Martinetz T., and Schulten K. Neural Computation and Self-Organizing Maps (1992), Addison-Wesley pp. 64-72
17. Roweis S.T., and Saul L.K. Nonlinear dimensionality reduction by locally linear embedding. Science 290 (2000) 2323-2326
18. Ruiz A., and López de Teruel P.E. Nonlinear kernel-based statistical pattern analysis. IEEE Transaction on Neural Networks 12 (2001) 16-32
19. Schölkopf B., Smola A., and Müller K.R. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 10 5 (1998) 1299-1319
20. Schölkopf B., Burges C., and Smola A.J. Advances in Kernel Methods - Support Vector Learning (1999), MIT Press, Cambridge
21. Ferdinando Samaria, Andy Harter, Parameterisation of a stochastic model for human face identification, in: Proceedings of Second IEEE Workshop on Applications of Computer Vision, 1994, pp. 138-142.
22. Tenenbaum J.B., de Silva V., and Langford J.C. A global geometric framework for nonlinear dimensionality reduction. Science 290 (2000) 2319-2323
23. Tsai C.-Y. On detecting nonlinear patterns in discriminant problems. Information Sciences 176 7 (2006) 772-798
24. Vapnik V. The Nature of Statistical Learning Theory (1995), Springer, New York
25. Yin H. Data visualisation and manifold mapping using the ViSOM. Neural Networks 15 8 (2002) 1005-1016
26. Yang J., Frangi A.F., Yang J.-y., Zhang D., and Jin Z. KPCA plus LDA: a complete kernel fisher discriminant framework for feature extraction and recognition. IEEE Transaction on Pattern Analysis and Machine Intelligence 27 2 (2005) 230-244
27. Zhu Z., He H., Starzyk J.A., and Tseng C. Self-organizing learning array and its application to economic and financial problems. Information Sciences 177 5 (2007) 1180-1192