Communications of the ACM
Machine Learning
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Benchmarking Least Squares Support Vector Machine Classifiers
Machine Learning
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
On the generalization of soft margin algorithms
IEEE Transactions on Information Theory
Face recognition using kernel direct discriminant analysis algorithms
IEEE Transactions on Neural Networks
A support vector machine formulation to PCA analysis and its kernel version
IEEE Transactions on Neural Networks
Bootstrap FDA for counting positives accurately in imprecise environments
Pattern Recognition
CARRADS: Cross layer based adaptive real-time routing attack detection system for MANETS
Computer Networks: The International Journal of Computer and Telecommunications Networking
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Representation and embedding are usually the two necessary phases in designing a classifier. Fisher discriminant analysis (FDA) is regarded as seeking a direction for which the projected samples are well separated. In this paper, we analyze FDA in terms of representation and embedding. The main contribution is that we prove that the general framework of FDA is based on the simplest and most intuitive FDA with zero within-class variance and therefore the mechanism of FDA is clearly illustrated. Based on our analysis, @e-insensitive SVM regression can be viewed as a soft FDA with @e-insensitive within-class variance and L"1 norm penalty. To verify this viewpoint, several real classification experiments are conducted to demonstrate that the performance of the regression-based classification technique is comparable to regular FDA and SVM.