In this short paper we shall consider the Kernel Fisher Discriminant Analysis (KFDA) and extend the idea of Linear Discriminant Analysis (LDA) to nonlinear feature space. We shall present a new method of choosing the optimal kernel function and its effect on the KDA classifier using information-theoretic complexity measure.
Bozdogan, H., Camillo, F., Liberati, C. (2006). On the choice of the kernel function in kernel discriminant analysis using information complexity. In S. Zani, A. Cerioli, M. Riani, M. Vichi (a cura di), Data Analysis, Classification and the Forward Search (pp. 11-21). Heidelberg : Springer-Verlag [10.1007/3-540-35978-8_2].
On the choice of the kernel function in kernel discriminant analysis using information complexity
LIBERATI, CATERINA
2006
Abstract
In this short paper we shall consider the Kernel Fisher Discriminant Analysis (KFDA) and extend the idea of Linear Discriminant Analysis (LDA) to nonlinear feature space. We shall present a new method of choosing the optimal kernel function and its effect on the KDA classifier using information-theoretic complexity measure.
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