关键词: 核Fisher判决分析, 核函数, 核空间, 支持向量机, 多类分类

Abstract: n the paper, theorys of Kernel Fisher Discriminant Analysis (KFDA) were researched and analysed. After applying KFDA in feature extracting, the performance of KFDA and that of Linear Fisher Discriminant Analysis (FDA) feature extracting algorithms were compared. Finally, a fast and simple multiclass classification algorithm of KFDA-based was proposed. The algorithm can classify multiclass fast and simply. First of all, multiclass samples were mapped into a high dimension kernel space. In the space, the same class samples were assembled together, the different class samples were perfectly separated. So the multiclass samples can be separate easily. Comparing with One-to-One algorithm and One-to-All algorithm, the experiment results indicate that our algorithm is certainly faster and simpler in classification than other two algorithms.

Key words: Kernel Fisher Discriminant Analysis(KFDA), kernel function, kernel space, support vector machines, multiclass classification