SMO'06 Proceedings of the 6th WSEAS International Conference on Simulation, Modelling and Optimization
Maximum Relative Margin and Data-Dependent Regularization
The Journal of Machine Learning Research
Feature Extraction Using Laplacian Maximum Margin Criterion
Neural Processing Letters
| Hi-index | 0.00 |
This paper proposes a novel nonlinear discriminant analysis method named by Kernerlized Maximum Average Margin Criterion (KMAMC), which has combined the idea of Support Vector Machine with the Kernel Fisher Discriminant Analysis (KFD). We also use a simple method to prove the relationship between both kernel methods. The difference of KMAMC from traditional KFD methods include (1) the within-class and between-class scatter matrices are computed based on the support vectors instead of all the samples; (2) multiple centers are exploited instead of the single center in computing the two scatter matrices; (3) the discriminant criteria is formulated as subtracting the trace of within-class scatter matrix from that of the between-class scatter matrix, therefore, the tedious singularity problem is avoided. These features have made KMAMC more practical for real-world applications. Our experiments on two face databases, the FERET and CAS-PEAL face database, have illustrated its excellent performance compared with some traditional methods such as Eigenface, Fisherface, and KFD.