Abstract
Linear Discriminant Analysis (LDA) is a famous supervised feature extraction method for subspace learning in computer vision and pattern recognition. In this paper, a novel method of LDA based on a new Maximum Correntropy Criterion optimization technique is proposed. The conventional LDA, which is based on L2-norm, is sensitivity to the presence of outliers. The proposed method has several advantages: first, it is robust to large outliers. Second, it is invariant to rotations. Third, it can be effectively solved by half-quadratic optimization algorithm. And in each iteration step, the complex optimization problem can be reduced to a quadratic problem that can be efficiently solved by a weighted eigenvalue optimization method. The proposed method is capable of analyzing non-Gaussian noise to reduce the influence of large outliers substantially, resulting in a robust classification. Performance assessment in several datasets shows that the proposed approach is more effectiveness to address outlier issue than traditional ones.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
- Mean Square Error
- Linear Discriminant Analysis
- Large Outlier
- Mean Square Error Criterion
- Average Reconstruction Error
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Geng, Y., Shan, C., Hao, P.: Square loss based regularized lda for face recognition using image sets. In: CVPRW, pp. 99–106 (2009)
Landon, J., Jeffs, B., Warnick, K.: Model-based subspace projection beamforming for deep interference nulling. IEEE Transactions on Signal Processing 60, 1215–1228 (2012)
McLachlan, G.J.: Discriminant analysis and statistical pattern recognition. Wiley (1992)
Zhao, W., Chellappa, R., Krishnaswamy, A.: Discriminant analysis of principal components for face recognition. In: 3rd International Conference on Automatic Face and Gesture Recognition (1998)
Li, X., Hu, W., Wang, H., Zhang, Z.: Linear discriminant analysis using rotational invariant l1 norm. Neurocomputing, 2571–2579 (2010)
Liu, W., Pokharel, P.P., Principe, J.C.: Correntropy: Properties and applications in non-gaussian signal processing. IEEE Trans. Signal Process 55, 5286–5298 (2007)
Parzen, E.: On estimation of a probability density function and mode. The Annals of Mathematical Statistics 33, 1065–1076 (1962)
Rockfellar, R.: Convex analysis. Princeton Univ., Princeton (1970)
Yuan, X., Hu, B.: Robust feature extraction via information theoretic learning. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 1193–1200 (2009)
Silverman, B.W.: Density estimation for statistics and data analysis. Chapman and Hall, London (1986)
http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html
Turk, M.A., Pentland, A.P.: Face recognition using eigenfaces. In: IEEE Conference on Computer Vision and Pattern Recognition (1991)
Kwak, N.: Principal component analysis based on L1-norm maximization. IEEE Trans. Pattern Anal. Mach. Intell. 30, 1672–1680 (2008)
Martnez, A., Benavente, R.: The ar-face database. CVC Technical Report 24 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhou, W., Kamata, Si. (2013). Linear Discriminant Analysis with Maximum Correntropy Criterion. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37331-2_38
Download citation
DOI: https://doi.org/10.1007/978-3-642-37331-2_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37330-5
Online ISBN: 978-3-642-37331-2
eBook Packages: Computer ScienceComputer Science (R0)