针对标签样本不易获得且需要大量的人力财力的问题，提出了基于稀疏表示的半监督线性子空间学习。该方法 的核心思想是将有标签样本与无标签样本合成一个训练集来训练半监督模型。首先将样本数据集进行训练得到字典以及稀 疏表示特征；然后将稀疏特征进行分组，分为含有较多识别信息（MDP）部分以及含有较少识别信息（LDP）部分；最后学习得 到半监督投影矩阵P。实验结果表明该方法在现有的人脸数据库extended Yale B和AR数据集上都取得了较好的结果。

Owing to labeling training samples are often hard to be collected and subject to a significant amount of human labor or costs, in this paper, a semi-supervised linear subspace learning approach via sparse coding is proposed. Our method can combine labeling samples and unlabeled samples to train a semi-supervised model. First, we train the samples to obtain the structured dictionary and the sparse representation feature; then we group them for more discriminative part (MDP) and less discriminative part (LDP). Finally, we learn the semi-supervised projection matrix P. The experimental results show that the proposed method can improve the classification results on the face image databases extended Yale B and AR datasets.