中文摘要：

针对人脸识别应用中的线性局部切空间排列算法（LLTSA）不能有效利用样本标签信息的问题,提出了一种线性局部切空间排列的标签传播半监督算法（SSLLTSA）。该算法利用标签传播的方法从带有部分标签的样本数据中得到软标签,然后利用软标签构造出软标签散度矩阵来描述数据集的类内紧凑性和类间分离性。SSLLTSA很好地保持了数据集的局部结构,有效地利用了样本中的标签信息。利用YALE和ORL人脸库进行实验,SSLLTSA比传统算法LLTSA的识别率平均分别提高了3.50%和3.89%。特别地,在只存有少量标签样本的情况下,该算法仍能保持良好的分类性能。

英文摘要：

Considering the limit that linear local tangent space alignment（ LLTSA） can＇t take advantage of the sample label information in face recognition application,this paper proposed a semi-supervised dimensionality reduction based on linear local tangent space alignment and label propagation（ SSLLTSA）. SSLLTSA used label propagation to get the soft labels in the sample data with part of labels. Then,it constructed the soft label based scatter matrices to describe the intra-class compactness and the inter-class separability. SSLLTSA used the information in the label effectively with the well preserved local structure of data. Through the experiments on YALE and ORL,SSLLTSA outperforms based on traditional dimensionality reduction algorithms with maximum average recognition rate by 3. 50% and 3. 89% respectively.