核最优变换与聚类中心的算法

基于核化原理，提出核最优变换与聚类中心算法．算法通过非线性变换，将数据映射到核空间，并在核空间中执行最优变换与聚类中心算法．该算法可提取稳健的非线性鉴别特征，解决复杂分布数据的模式分类问题．同时，基于训练样本在核空间所张成的子空间的一组基，提出一个快速提取鉴别特征的计算方法，解决了一般核方法面临的“大训练集”难题．基于IRIS，YEAST，GLASS等数据的分类实验验证了该方法的有效性．

Kernel optimal transformation and cluster centers algorithm

The kernel optimal transformation and cluster centers algorithm (KOT-CC) is presented by using kernel methods. In the KOT-CC, all data are mapped to a kernel space via some nonlinear mapping and the optimal transformation and cluster centers (OT-CC) is performed in the kernel space. KOT-CC is a powerful technique for extracting nonlinear discriminant features and is very effective in solving pattern recognition problems which have serious overlap between the patterns of different classes. A fast algorithm for KOT-CC is also proposed based on the basis of the sub-space which is spanned by the training samples mapped into the kernel space, which can improve the efficiency of the feature extraction process and tackle the “large sample size” problem which many kernel methods may suffer from. The experiments based on the data of IRIS, YEAST, GLASS and so on, demonstrate the validity of the proposed new algorithm.