Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners
IEEE Transactions on Pattern Analysis and Machine Intelligence
The nature of statistical learning theory
The nature of statistical learning theory
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Regularized discriminant analysis for the small sample size problem in face recognition
Pattern Recognition Letters
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Nonlinear kernel-based statistical pattern analysis
IEEE Transactions on Neural Networks
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Face recognition using kernel direct discriminant analysis algorithms
IEEE Transactions on Neural Networks
Orthogonal Quadratic Discriminant Functions for Face Recognition
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Bagging Constraint Score for feature selection with pairwise constraints
Pattern Recognition
Short communication: Diagnosis of bladder cancers with small sample size via feature selection
Expert Systems with Applications: An International Journal
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It is generally believed that quadratic discriminant analysis (QDA) can better fit the data in practical pattern recognition applications compared to linear discriminant analysis (LDA) method. This is due to the fact that QDA relaxes the assumption made by LDA-based methods that the covariance matrix for each class is identical. However, it still assumes that the class conditional distribution is Gaussian which is usually not the case in many real-world applications. In this paper, a novel kernel-based QDA method is proposed to further relax the Gaussian assumption by using the kernel machine technique. The proposed method solves the complex pattern recognition problem by combining the QDA solution and the kernel machine technique, and at the same time, tackles the so-called small sample size problem through a regularized estimation of the covariance matrix. Extensive experimental results indicate that the proposed method is a more sophisticated solution outperforming many traditional kernel-based learning algorithms.