A pre-clustering technique for optimizing subclass discriminant analysis

Pattern Recognition Letters

Volumn 31, Issue 6, 2010, Pages 462-468

  Kim, Sang Woon ^a  

^a Myong Ji University   (South Korea)

Author keywords

Dimensionality reduction; Linear discriminant analysis (LDA); Pre clustering technique; Subclass discriminant analysis (SDA)

Indexed keywords

ARTIFICIAL DATA; CLASS DISTRIBUTIONS; CLASSIFICATION ACCURACY; CLUSTERING TECHNIQUES; COMPUTATIONAL BURDEN; COMPUTATIONAL COSTS; CONDITIONAL DISTRIBUTION; DIMENSIONALITY REDUCTION; DIMENSIONALITY REDUCTION METHOD; IMAGE DATABASE; K-MEANS CLUSTERING; LINEAR DISCRIMINANT ANALYSIS; MIXTURE OF GAUSSIANS; POSSIBLE SOLUTIONS; PRE-CLUSTERING TECHNIQUE; REDUCTION OF DIMENSIONALITY; TRAINING SETS; UNDERLYING DISTRIBUTION;

COMPUTATIONAL COMPLEXITY; DISCRIMINANT ANALYSIS; IMAGE RETRIEVAL; IMAGE SEGMENTATION;

CLUSTER ANALYSIS;

EID: 77249109292     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2009.07.007     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 Trans. Pattern Anal. Machine Intell. 19 7 (1997) 711-720
2. Cevikalp H., Neamtu M., Wilkes M., and Barkana A. Discriminative common vectors for face recognition. IEEE Trans. Pattern Anal. Machine Intell. 27 1 (2005) 4-13
3. Chen, H.-T., Chang, H.-W., Liu, T.-L., 2000. Local discriminant embedding and its variants. In: Proc. IEEE Comput. Soc. Conf. on Computer Vision and Pattern Recognition, vol. 2, pp. 846-853.
4. Dai D.Q., and Yuen P.C. Regularized discriminant analysis and its application to face recognition. Pattern Recognition 36 (2003) 845-847
5. Friedman M., and Kandel A. Introduction to Pattern Recognition - Statistical, Structural, Neural and Fuzzy Logic Approaches (1999), World Scientific, New Jersey
6. Fraley C., and Raftery A.E. How many clusters ? Which clustering method ? Answers via model-based cluster analysis. Comput. J. 41 8 (1998) 578-588
7. Fukunaga K. Introduction to Statistical Pattern Recognition. second ed. (1990), Academic Press, San Diego
8. Harter A., Hopper A., Steggles P., Ward A., and Webster P. The anatomy of a context-aware application. Wirel. Netw. 8 (2001) 187-197
9. He, D., Wang, J., 2002. Parallel computing of eigenvalue of doubley stochastic matrix. In: Proc. 5th Int. Conf. on Algorithms and Architecture for Parallel Processing (ICA3pp02), Beijing, China, pp. 355-358.
10. Howland P., Wang J., and Park H. Solving the small sample size problem in face reognition using generalized discriminant analysis. Pattern Recognition 39 (2006) 277-287
11. Jain A.K., Duin R.P.W., and Mao J. Statistical pattern recognition: A review. IEEE Trans. Pattern Anal. Machine Intell. 22 1 (2000) 4-37
12. Kim, S.-W., Duin, R.P.W., 2007. On using a pre-clustering technique to optimize LDA-based classifiers for appearance-based face recognition. In: Proc. 12th Iberoamerican Congress on Pattern Recognition (CIARP 2007). Lect. Notes Comput. Sci., 4756, 466-476.
13. Kim T.-K., and Kittler J. Locally linear discriminant analysis for multimodally distributed classes for face recognition with a single model image. IEEE Trans. Pattern Anal. Machine Intell. 27 3 (2005) 318-327
14. Loog M., and Duin R.P.W. Linear dimensionality reduction via a heteroscedastic extension of LDA: The Cherno criterion. IEEE Trans. Pattern Anal. Machine Intell. 26 6 (2004) 732-739
15. Ming L., and Yuan B. 2D-LDA: A statistical linear discriminant analysis for image matrix. Pattern Recognition Lett. 26 5 (2005) 527-532
16. Price J.R., and Gee T.F. Face recognition using direct, weighted linear discriminant analysis and modular subspaces. Pattern Recognition 38 (2005) 209-219
17. Roweis S., and Saul L.K. Nonlinear dimensionality reduction by locally linear embedding. Science 290 (2000) 2323-2326
18. Rueda L., and Herrera M. Linear dimensionality reduction by maximizing the Chernoff distance in the transformed space. Pattern Recognition 41 (2008) 3138-3152
19. Sebastian, T.B., Klein, P.N., Kimia, B.B., 2001. Recognition of shapes by editing shock graphs. In: Proc. Int. Conf. on Computer Vision, pp. 755-762.
20. Yang J., Frangi A.F., Yang J.-Y., and Zhang D. KPCA plus LDA: A complete kernel Fisher discriminant framework for feature extraction and recognition. IEEE Trans. Pattern Anal. Machine Intell. 27 2 (2005) 230-244
21. Yang, M.-H., 2002. Kernel Eigenfaces vs. kernel Fisherfaces: Face recognition using kernel methods. In: Proc. 5th IEEE Int. Conf. on Automatic Face and Gesture Recognition, pp. 215-220.
22. Ye J., and Li Q. A two-stage linear discriminant analysis via QR-decomposition. IEEE Trans. Pattern Anal. Machine Intell. 27 6 (2005) 929-941
23. Yu H., and Yang J. A direct LDA algorithm for high-dimensional data - with application to face recognition. Pattern Recognition 34 (2001) 2067-2070
24. Zheng W.-S., Lai J.H., and Li S.Z. 1D-LDA vs. 2D-LDA: When is vector-based linear discriminant analysis better than matrix-based?. Pattern Recognition 41 (2008) 2156-2172
25. Zhu M., and Martinez A.M. Subclass discriminant analysis. IEEE Trans. Pattern Anal. Machine Intell. 28 8 (2006) 1274-1286
26. Zohar H., and Aladjem M. Model-based mixture discriminant analysis - an experimental study. Pattern Recognition 38 (2005) 437-440
27. Zohar H., and Aladjem M. Regularized mixture discriminant analysis. Pattern Recognition Lett. 28 (2007) 2104-2115