Info-margin maximization for feature extraction

Pattern Recognition Letters

Volumn 30, Issue 16, 2009, Pages 1516-1522

  Qiu, Xipeng ^a   Wu, Lide ^a  

^a FUDAN UNIVERSITY   (China)

Author keywords

Info margin maximization; Linear feature extraction

Indexed keywords

DIVERGENCE MEASURES; FAST CONVERGENT ALGORITHM; FEATURE EXTRACTION METHODS; GAUSSIAN KERNELS; GENERALIZATION ERROR; INFO-MARGIN MAXIMIZATION; INFORMATION DIVERGENCE; LINEAR FEATURE EXTRACTION; NOVEL METHODS; PARZEN WINDOWS; QUADRATIC ENTROPY;

DATA VISUALIZATION; INFORMATION THEORY; OPTIMIZATION;

FEATURE EXTRACTION;

EID: 70350124939     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2009.08.006     Document Type: Article

References (24)

1. Asuncion, A., Newman, D., 2007. UCI machine learning repository. URL: .
2. Belhumeur P., Hespanda J., and Kiregeman D. Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Trans. Pattern Anal. Machine Intell. 19 7 (1997) 711-720
3. Bian Z., and Zhang X. Pattern Recognition. second ed. (1999), Tsinghua University, Beijing, China
4. Bollacker, K., Ghosh, J., 1996. Linear feature extractors based on mutual information. In: Proc. 13th ICPR, pp. 720-724.
5. Chen L., Liao H., Ko M., Lin J., and Yu G. A new LDA-based face recognition system which can solve the small sample size problem. Pattern Recogn. 33 10 (2000) 1713-1726
6. Cover T.M., and Thomas J.A. Elements of Information Theory (1991), Wiley-Interscience, New York, NY, USA
7. Crammer, K., Gilad-Bachrach, R., Navot, A., Tishby, N., 2002. Margin analysis of the lvq algorithm. In: Proc. Neural Information Processing System (NIPS).
8. Friedman J.H. Exploratory projection pursuit. J. Amer. Statist. Assoc. 82 397 (1987) 249-266
9. Fukunaga K. Introduction to Statistical Pattern Recognition. second ed. (1990), Academic Press, Boston
10. Fukunaga K., and Mantock J. Nonparametric discriminant analysis. IEEE Trans. Pattern Anal. Machine Intell. 5 (1983) 671-678
11. Graham, D.B., Allinson, N.M., 1998. Characterizing virtual eigensignatures for general purpose face recognition. (in) Face Recognition: From Theory to Applications, NATO ASI Series F, Computer and Systems Sciences 163, 446-456.
12. Hild K.E., Erdogmus D., Torkkola K., and Principe J.C. Feature extraction using information-theoretic learning. IEEE Trans. Pattern Anal. Machine Intell. 28 9 (2006) 1385-1392
13. Kapur J. Measures of Information and their Applications (1994), Wiley, New Delhi, India
14. Linsker R. Self-organization in a perceptual network. IEEE Computer 21 3 (1988) 105-117
15. Lyu, S., 2005. Infomax boosting. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR).
16. Principe J.C., Xu D., and Fisher III J.W. Information theoretic learning. Unsupervised Adaptive Filtering (2000), Wiley, New York, NY, USA 265-319
17. Rosset, S., Zhu, J., Hastie, T., 2003. Margin maximizing loss functions. In: Proc. Neural Information Processing System (NIPS).
18. Samaria, F., Harter, A., 1994. Parameterisation of a stochastic model for human face identification. In: Proc. 2nd IEEE Workshop on Applications of Computer Vision.
19. Schölkopf B., Smola A., and Müller K.-R. Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput. 10 (1998) 1299-1319
20. Torkkola K. Feature extraction by non-parametric mutual information maximization. J. Mach. Learn. Res. 3 (2003) 1415-1438
21. Turk M., and Pentland A. Eigenfaces for recognition. J. Cognitive Neurosci. 3 1 (1991) 71-86
22. Vapnik V.N. The Nature of Statistical Learning Theory (1995), Springer-Verlag New York, Inc., New York, NY, USA
23. Vasconcelos, N., 2002. Feature selection by maximum marginal diversity. In: Proc. Neural Information Processing System (NIPS).
24. Weisstein, E.W., 2006. L'hospital's rule. From MathWorld - A Wolfram Web Resource. .