中文摘要：

基分类器的差异性对于集成学习来说至关重要，从直观上讲，对约束重采样有潜力获得比对样本重采样更好的多样性．文中在典型相关分析算法基础上，通过引入成对约束作为监督信息对样本进行特征抽取从而形成新的训练数据．算法中集成学习的思想主要体现在成对约束的选取上，对约束进行随机重采样以获得具有多样性的基分类器．在多特征手写体数据集以及人脸数据集（Yale和AR）上进行实验考察该算法随选取的约束比例变化的情况，结果表明该方法获得比传统集成学习方法更好的性能．

英文摘要：

The diversity among base classifiers is crucial for ensemble learning, and intuitively resampling pairwise constraints get better diversity than resampling instances. The supervision information in the form of pairwise constraints is introduced for feature extraction of samples to generate new training data based on canonical correlation analysis （CCA）. In this algorithm, the spirit of ensemble learning is embodied in the way to select constraints. The constraints are resampled randomly to get the diverse base classifiers on muhiview data. The experiments are carried out on multiple feature database and Yale and AR facial databases, and the results show that the proposed ensemble method achieves better performance than the conventional ensemble learning methods.