The novel supervised learning method of vertex discriminant analysis (VDA) has been demonstrated for its good performance in multicategory classification. The current paper explores an elaboration of VDA for nonlinear discrimination. By incorporating reproducing kernels, VDA can be generalized from linear discrimination to nonlinear discrimination. Our numerical experiments show that the new reproducing kernel‐based method leads to accurate classification for both linear and nonlinear cases. © 2012 Wiley Periodicals, Inc. Statistical Analysis and Data Mining, 2012

中文翻译：

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具有再现核的非线性顶点判别分析。


顶点判别分析（VDA）的新颖监督学习方法已在多类别分类中表现出良好的性能。本论文探讨了 VDA 用于非线性判别的详细阐述。通过合并复制内核，VDA 可以从线性判别推广到非线性判别。我们的数值实验表明，新的基于再现核的方法可以对线性和非线性情况进行准确分类。 © 2012 Wiley periodicals, Inc. 统计分析和数据挖掘，2012 年