中文摘要：

提出两个判别性的特征融合方法——主成分判别性分析和核主成分判别性分析．基于主成份分析和最大间隔准则理论，构造一个多目标规划模型作为特征融合的目标．随后，该模型被转化成一个单目标规划问题并通过特征分解的方法求解．此外，将一个近似分块对角核矩阵眉分成C（C为数据集中的类别数）个小矩阵，并求出它们的特征值和特征向量．在此基础上，通过向量代数处理得到一个映射矩阵α，当核矩阵眉投影到α上，同类样本的相似信息能最大程度地得到保持．本文中的实验证实两种方法的有效性．

英文摘要：

Two methods for dimensionality reduction, principal component discriminative analysis and kernel principal component discriminative analysis, are proposed. Based on the theory of principal component analysis and maximum margin criterion, a multi-objective project model is constructed to formalize the goals for feature fusion. Then, it is transformed into a single-objective cost function for the projection, and the optimal linear mapping is obtained through optimizing this cost function. Additionally, the nearly diagonal block kernel matrix is divided into c kernel matrixes （ c is the number of classes in dataset）, and eigen-decomposition method is used to solve their d principal vectors. Through the process of vector algebra, a combined mapping α is obtained. When the original kernel matrix K is projected on α, the inner-class information is optimally preserved. The experimental results show their validity.