鉴别分析方法的最优化问题研究

提出了一种核Fisher鉴别分析方法优化方案,并分别给出了解决两类分类和解决多于两类的分类问题的算法,该方案具有明显的分类效率上的优势。在这种方案的实现中,首先从总体训练样本中选择出“显著”训练样本,对测试样本的分类只依赖于测试样本与“显著”训练样本之间的核函数。还设计出了一种选择“显著”训练样本的递归算法,以降低算法的计算复杂度。将该算法应用于人脸图象数据库与“基准”数据集,得到了很好的实验效果。

Research on optimizing kernel discriminant analysis

In this paper an optimized scheme on kernel Fisher discriminant method is proposed,which is with superiority in classification efficiency.The corresponding algorithms are developed for two-class problems and multi-class problems,respectively.In the scheme,only a part of training patterns,called “significant nodes”,are necessary to be adopted in classifying one test pattern.A recursive algorithm for selecting“significant nodes” is also presented in detail.The experiments on benchmarks and face image databases show that the novel method is effective and much efficient in classifying.