We combine linear discriminant analysis (LDA) and K-means clustering into a coherent framework to adaptively select the most discriminative subspace. We use K-means clustering to ...
In Kernel Fisher discriminant analysis (KFDA), we carry out Fisher linear discriminant analysis in a high dimensional feature space defined implicitly by a kernel. The performance...
Seung-Jean Kim, Alessandro Magnani, Stephen P. Boy...
In this paper, we discuss fuzzy classifiers based on Kernel Discriminant Analysis (KDA) for two-class problems. In our method, first we employ KDA to the given training data and ca...
Mika et al. [3] introduce a non-linear formulation of Fisher's linear discriminant, based the now familiar "kernel trick", demonstrating state-of-the-art performanc...
Abstract. To generalize the Fisher Discriminant Analysis (FDA) algorithm to the case of discriminant functions belonging to a nonlinear, finite dimensional function space F (Nonli...