Robust feature extraction via information theoretic learning

ACM International Conference Proceeding Series

Volumn 382, Issue , 2009, Pages

  Yuan, Xiao Tong ^a   Hu, Bao Gang ^a  

^a INSTITUTE OF AUTOMATION   (China)

Author keywords

[No Author keywords available]

Indexed keywords

CLASS LABELS; INFORMATION POTENTIAL; INFORMATION THEORETIC LEARNING; M-ESTIMATORS; OBJECTIVE FUNCTIONS; REAL WORLD DATA;

EDUCATION; INFORMATION THEORY; LABELS; OPTIMIZATION; ROBOT LEARNING;

FEATURE EXTRACTION;

EID: 70049107180     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1553374.1553526     Document Type: Conference Paper

References (21)

1. Belkin, M., Niyogi, P., & Sindhwani, V. (2006). Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research, 2399-2434
2. Cai, D., He, X., & Han, J. (2008). Srda: An efficient algorithm for large-scale discriminant analysis. IEEE Transactions on Knowledge and Data Engineering, 20(1), 1-12
3. Cayton, L., & Dasgupta, S. (2006). Robust euclidean embedding. International Conference on Machine Learning (pp. 169-176)
4. Chang, H., & Yeung, D.-Y. (2006). Robust locally linear embedding. Pattern Recognition, 39(6), 1053-1065
5. Friedman, J. (1989). Regularized discriminative analysis. Journal of American Statistical Association, 84(405), 165-175
6. Fukunnaga, K. (1991). Introduction to statistical pattern recognition. Academic Press.
7. He, X., & Niyogi, P. (2004). Locality preserving projections. Advances in Neural Information Processing Systems 16. Cambridge, MA: MIT Press.
8. Hild-II, K., Erdogmus, D., Torkkola, K., & Principe, C. (2006). Feature extraction using informationtheoretic learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(9), 1385-1392.
9. Huber, P. (1981). Robust statistics. Wiley.
10. Jenssen, R., Eltoft, T., Girolami, M., & Erdogmus, D. (2006). Kernel maximum entropy data transformation and an enhanced spectral clustering algorithm. Advances in Neural Information Processing Systems 19 (pp. 633-640). Cambridge, MA: MIT Press.
11. Joliffe, I. (1986). Principal component analysis. Springer-Verlag.
12. Kaski, S., & Peltonen, J. (2003). Informative discriminant analysis. International Conference on Machine Learning (pp. 329-336).
13. Liu, W., Pokharel, P. P., & Principe, J. C. (2007). Correntropy: Properties and applications in nongaussian signal processing. IEEE Transactions on Signal Processing, 55(11), 5286-5298.
14. Mizera, I., & Muller, C. (1999). Breakdown points and variation exponents of robust m-estimators in linear models. Annals of Statistics, 27, 1164-1177.
15. Mizera, I., & Muller, C. (2002). Breakdown points of cauchy regression-scale estimators. Statistics and Probability Letters, 57, 79-89.
16. Principe, J., Xu, D., & Fisher, J. (2000). Information theoretic learning. Unsupervised Adaptive Filtering. New York: Wiley.
17. Renyi, A. (1961). On measures of information and entropy. Proceedings of the 4th Berkeley Symposium on Mathematics, Statistics and Probability (pp. 547-561).
18. Rockfellar, R. (1970). Convex analysis. Princeton Press.
19. Torkkola, K. (2003). Feature extraction by nonparametric mutual information maximization. Journal of Machine Learning Research, 3, 1415-1438.
20. Torre, F., & Black, M. (2001). Robust principal component analysis for computer vision. International Conference on Computer Vision (pp. 362-369).
21. Yan, S., Xu, D., Zhang, B., Zhang, H., Yang, Q., & Lin, S. (2007). Graph embedding and extensions: A general framework for dimensionality reduction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(1), 40-51.