An adaptive nonparametric discriminant analysis method and its application to face recognition
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
A novel local sensitive frontier analysis for feature extraction
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Push-Pull marginal discriminant analysis for feature extraction
Pattern Recognition Letters
Nonparametric marginal Fisher analysis for feature extraction
ICIC'10 Proceedings of the Advanced intelligent computing theories and applications, and 6th international conference on Intelligent computing
Robust linearly optimized discriminant analysis
Neurocomputing
A supervised non-linear dimensionality reduction approach for manifold learning
Pattern Recognition
Feature extraction via balanced average neighborhood margin maximization
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Super-class Discriminant Analysis: A novel solution for heteroscedasticity
Pattern Recognition Letters
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Linear Discriminant Analysis (LDA) is a popular feature extraction technique in face recognition. However, it often suffers from the small sample size problem when dealing with the high dimensional data. Moreover, while LDA is guaranteed to find the best directions when each class has a Gaussian density with a common covariance matrix, it can fail if the class densities are more general. In this paper, a new nonparametric linear feature extraction method, step-wise nonparametric margin maximum criterion(SNMMC), is proposed to find the most discriminant directions, which does not assume that the class densities belong to any particular parametric family and does not depend on the non-singularity of the within-class scatter matrix either. On three datasets from ATT and FERET face databases, our experimental results demonstrate that SNMMC outperforms other methods and is robust to variations of pose, illumination and expression.