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中文摘要：
在模式特征子空间中选取一组标准正交向量，使用这组向量可以生成大量的虚拟训练样本，从而实现对协方差矩阵的优化．在ORL人脸库上的实验表明，优化后协方差矩阵的特征值均显著变大，使该矩阵的逆阵稳定性得到了提高．利用优化的协方差矩阵对正则化判别分析方法进行优化，其模式分类正确率有显著提高．
英文摘要：
In this paper, an improvement is made through selecting a group of normal orthogonal vectors in feature subspace, to generate large amount of virtual training samples. The experimental results on ORL face database show that by the proposed improved method, eigenvalues of the optimized covariance matrices become larger and the matrices inverses become more stable. By using the optimized covariance matrices to optimize the new regularized discriminant analysis （RDA）, the correct classification rate is higher than that by the old RDA.
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