基于一种改进的类内散布矩阵的核鉴别分析法

基于核的非线性判别方法及算法的研究近年来得到广泛的研究。在这些方法中，一个主要的缺点是对L类判别问题，判别向量最多只有L-1]个。定义一种改进的核类间散布矩阵，并对两类问题给出改进的核鉴别分析法，该方法克服了以上缺陷。试验结果表明所提出的方法与其他方法相比具有很好的识别性能。

Novel kernel-based nonlinear discriminant analysis

Kernel-based nonlinear Discriminant Analysis(KDA)has attracted much attention due to the high performance and kernel-based nonlinear discriminant algorithms are developed.Among these algorithms,one of its major disadvantages is that the maximal number of its discriminating vectors capable to be found is limited by the number of classes involved,i.e.,L-1 for L-class problem.For binary-class problem,it proposes a new KDA to break through the inherent limitation by a special form of kernel between-class scatter-matrix.Experimental results show that the approach gives impressive recognition performances compared to both the Alternative Fisher Linear Discriminant Analysis(AFLDA)and the Fisher Linear Discriminant Analysis(FLDA).