Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
ACM Transactions on Graphics (TOG)
Facial expression recognition: a clustering-based approach
Pattern Recognition Letters
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
Face Recognition Using Kernel Based Fisher Discriminant Analysis
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Journal of Cognitive Neuroscience
Possibilistic approach to kernel-based fuzzy c-means clustering with entropy regularization
MDAI'05 Proceedings of the Second international conference on Modeling Decisions for Artificial Intelligence
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
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In this paper, the 3D head model classification problem is addressed by use of a newly developed subspace analysis method: kernel clustering-based discriminant analysis or KCDA as an abbreviation. This method works by first mapping the original data into another high-dimensional space, and then performing clustering-based discriminant analysis in the feature space. The main idea of clustering-based discriminant analysis is to overcome the Gaussian assumption limitation of the traditional linear discriminant analysis by using a new criterion that takes into account the multiple cluster structure possibly embedded within some classes. As a result, Kernel CDA tries to get through the limitations of both Gaussian assumption and linearity facing the traditional linear discriminant analysis simultaneously. A novel application of this method in 3D head model classification is presented in this paper. A group of tests of our method on 3D head model dataset have been carried out, reporting very promising experimental results.