为克服线性判别分析（LDA）只能利用有标记样本的缺点,提出一种基于局部重构与全局保持的半监督判别分析（LRGPSSDA）方法.LRGPSSDA通过最小化局部重构误差来确定邻域图的边权值,在保持数据集局部结构的同时保持其全局结构,具有对邻域参数的选择不敏感、所得投影子空间的维数不受样本类别数的限制等特点.相较现有的半监督判别分析方法（如SDA和UDA）,LRGPSSDA的分类性能更好.在YaleB和CMUPIE标准人脸库上的实验结果验证了该算法的有效性.

Linear discriminant analysis （ LDA） can only make use of labeled samples. In order to overcome this shortcoming,a new semi-supervised discriminant analysis method based on local reconstruction and global preserving marked as LRGPSSDA is proposed. LRGPSSDA sets the edge weight of neighborhood graph by minimizing the local reconstruction error and preserves the global geometric structure of the sampled data set without destroying its local geometric structure. It is insensitive to the selection of neighborhood parameter,and the dimensionality of its projection subspace is independent of the number of sample classes. As compared with the existing semi-supervised discriminant analysis methods such as SDA and UDA,LRGPSSDA is of higher classification performance. The experimental results of YaleB and CMU PIE face database also demonstrate that LRGPSSDA is effective.