Robust linearly optimized discriminant analysis

Neurocomputing

Volumn 79, Issue , 2012, Pages 140-157

  Zhang, Zhao ^a   Chow, Tommy W S ^a  

^a CITY UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Dimensionality reduction; Image recognition; Linear discriminant analysis; Marginal inter and intra class scatters; Orthogonality; Robustness

Indexed keywords

DIMENSIONALITY REDUCTION; EFFECTIVE SOLUTION; FISHER LINEAR DISCRIMINANT ANALYSIS; GAUSSIAN DENSITY; GENERAL CLASS; GEOMETRICAL INTERPRETATION; HIGH DIMENSIONAL DATA; LDA TECHNIQUE; LINEAR DISCRIMINANT ANALYSIS; MARGINAL CRITERIA; MARGINAL INTER- AND INTRA-CLASS SCATTERS; MAXIMUM CLASS; MULTI-MODAL; ORTHOGONALITY; PROJECTION MATRIX; SINGULARITY PROBLEMS;

FACE RECOGNITION; IMAGE RECOGNITION; OPTIMIZATION; ROBUSTNESS (CONTROL SYSTEMS);

DISCRIMINANT ANALYSIS;

ACCURACY; ARTICLE; CALCULATION; CONTROLLED STUDY; DATA BASE; DISCRIMINANT ANALYSIS; FISHER LINEAR DISCRIMINANT ANALYSIS; INTERMETHOD COMPARISON; LINEARLY OPTIMIZED DISCRIMINANT ANALYSIS; PRIORITY JOURNAL; SIMULATION; VALIDITY;

EID: 83955165277     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2011.10.015     Document Type: Article

References (35)

1. L. Maaten, E. Postma, J. Herik, Dimensionality Reduction: A Comparative Review. Tilburg University Technical Report, TiCC-TR 2009-005, 2009.
2. Fukunaga K. Introduction to Statistical Pattern Recognition 1990, Academic Press, Boston. 2nd edition.
3. Martinez A.M., Kak A.C. PCA versus LDA. IEEE Trans. Pattern Anal. Mach. Intell. 2001, 23(2):228-233.
4. Belhumeour P.N., Hespanha J.P., Kriegman D.J. Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 1997, 19(7):711-720.
5. Chen L.F., Liao H.Y.M., Lin J.C., Kao M.D., Yu G.J. A new LDA-based face recognition system which can solve the small sample size problem. Pattern Recog. 2000, 33(10):1713-1726.
6. Ye J.P. Characterization of a family of algorithms for generalized discriminant analysis on undersampled problems. J. Mach. Learning Res. 2005, 6:483-502.
7. Yu H., Yang J. A direct LDA algorithm for high-dimensional data-with application to face recognition. Pattern Recog. 2001, 34:2067-2070.
8. X.P. Qiu, D. Wu, Face recognition by stepwise nonparametric margin maximum criterion, in: Proceedings of the Tenth IEEE International Conference on Computer Vision, 2005, pp. 1567-1572.
9. Ji S.W., Ye J.P. Generalized linear discriminant analysis: a unified framework and efficient model selection. IEEE Trans. Neural Networks 2008, 19(10):1768-1782.
10. Belkin M., Niyogi P. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput. 2003, 15(6):1373-1396.
11. Roweis S., Saul L. Nonlinear dimensionality reduction by locally linear embedding. Science 2000, 290(5500):2323-2326.
12. X.G. Wang, X.O. Tang, Dual-space linear discriminant analysis for face recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2004. pp. 564-569.
13. Fukunaga K., Mantock J. Nonparametric discriminant analysis. IEEE Trans. Pattern Anal. Mach. Intell. 1983, 5:671-678.
14. Bressan M., Vitriỳa J. Nonparametric discriminant analysis and nearest neighbor classification. Pattern Recog. Lett. 2003, 24:2743-2749.
15. X.P. Qiu, D. Wu, Stepwise nearest neighbor discriminant analysis, in: Proceedings of the International Joint Conference on Artificial Intelligence, 2005, pp. 829-835.
16. Zheng Y.J., Yang J.Y., Yang J., Wu X.J. Nearest neighbour line nonparametric discriminant analysis for feature extraction. Electron. Lett. 2006, 42(12):679-680.
17. Hull J. A database for handwritten text recognition research. IEEE Trans. Pattern Anal. Mach. Intell. 1994, 16(5):550-554.
18. Li H., Jiang T., Zhang K. Efficient and robust feature extraction by maximum margin criterion. IEEE Trans. Neural Network 2006, 17(1):157-165.
19. Jia Y., Nie F., Zhang C.S. Trace ratio problem revisited. IEEE Trans. Neural Network 2009, 20(4):729-735.
20. Sugiyama M. Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis. J. Mach. Learn. Res. 2007, 8:1027-1061.
21. Guo Y., Li S., Yang J., Shu T., Wu L. A generalized Foley-Sammon transform based on generalized fisher discriminant criterion and its application to face recognition. Pattern Recog. Lett. 2003, 24(1-3):147-158.
22. H. Wang, S. Yan, D. Xu, X. Tang, T. Huang, Trace ratio vs. ratio trace for dimensionality reduction, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, 2007, pp. 1-8.
23. He X., Yan S., Hu Y., Niyogi P., Zhang H. Face recognition using laplacianfaces. IEEE Trans. Patten Anal. Mach. Intell. 2005, 27(3):228-340.
24. B. Leibe, B. Schiele, Analyzing appearance and contour based methods for object categorization, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2003, pp. 409-415.
25. Loog M. Approximate Pairwise Accuracy Criteria for Multiclass Linear Dimension Reduction: Generalisations of the Fisher Criterion 1999, Delft Univ. Press.
26. Hou C.P., Wang J., Wu Y., Yi D.Y. Local linear transformation embedding. Neurocomputing 2009, 72(10-12):2368-2378.
27. Loog M., Duin R.P.W., Haeb-Umbach R. Multiclass linear dimension reduction by weighted pairwise fisher criteria. IEEE Trans. Patten Anal. Mach. Intell. 2001, 23(7):762-766.
28. Sun T., Chen S. Class label versus sample label-based CCA. Appl. Math. Comput. 2007, 185(1):272-283.
29. Hardoon D.R., Szedmak S., Shawe-Taylor J. Canonical correlation analysis: an overview with application to learning methods. Neural Comput. 2004, 16(12):2639-2664.
30. Tanga E.K., Suganthana P.N., Yaob X., Qina A.K. Linear dimensionality reduction using relevance weighted LDA. Pattern Recog. 2005, 38(4):485-493.
31. Schölkopf B., Smola A. Learning with Kernels 2002, MIT Press, Cambridge, MA, (pp. 25-55).
32. Fiori S. Visualization of Riemannian-manifold-valued elements by multidimensional scaling. Neurocomputing 2011, 74(6):983-992.
33. Li J., Li X.L., Tao D.C. KPCA for semantic object extraction in images. Pattern Recog. 2008, 41(10):3244-3250.
34. Yao F.Y., Jing X.Y., Wong H.S. Face and palmprint feature level fusion for single sample biometrics recognition. Neurocomputing 2007, 70:1582-1586.
35. Yang J., Frangi A.F., Yang J.Y., Zhang D., Jin Z. KPCA Plus LDA: A Complete Kernel Fisher Discriminant Framework for Feature Extraction and Recognition. IEEE Trans. Pattern Anal. Mach. Intell. 2005, 27(2):234-244.