中文摘要：

在典型相关分析算法（canonical correlation analysis，简称CCA）的基础上，通过引入以成对约束形式给出的监督信息，提出了一种半监督的典型相关分析算法（Semi—CCA）．在此算法中，除了考虑大量的无标号样本以外，还考虑成对约束信息，即已知两样本属于同一类（正约束）或不属于同一类（负约束），同时验证了两者的相对重要性．在人工数据集、多特征手写体数据集和人脸数据集（Yale和AR）上的实验结果表明，Semi-CCA能够有效地利用少量的监督信息来提高分类性能．

英文摘要：

In this paper, a semi-supervised canonical correlation analysis algorithm called Semi-CCA is developed, which uses supervision information in the form of pair-wise constraints in canonical correlation analysis （CCA）. In this setting, besides abundant unlabeled data examples, the domain knowledge in the form of pair-wise constraints which specify whether a pair of data examples belongs to the same class （must-link constraints） or not （cannot-link constraints） is also available. Meanwhile, the relative importance of must-link constraints and cannot-link constraints is validated. Experimental results on the artificial dataset, multiple feature database and facial database including Yale and AR show that the proposed Semi-CCA can effectively enhance the classifier performance by using only a small amount of supervision information.