A multi-view paired model, cross-view constraint, is taken into account and thus the pairwise constraints are extended in single-view learning. Instead of the strict paired constraints, the weaker constraint information is used, i. e. whether the data pairs between different views belong to the same class or not. Therefore, the cross-view constraints can not only include the totally paired constraints, but also be generalized to the case that the data are unpaired completely. Based on the cross-view constraints, a multi-view classification method is proposed. The proposed method can deeply mine the potential discriminative information in cross-view constraints and utilize the structural information of the data pairs as well. Experimental results demonstrate the effectiveness of the proposed method.%考虑一种多视图数据配对形式---跨视图约束，从而推广单视图学习中的成对约束。利用不同视图间数据对是否属于同一类的弱化约束信息，代替严格的配对约束，不仅涵盖原有的一一配对，而且能推广到完全无配对的情况。提出一种基于跨视图约束的多视图分类方法，该方法不仅能深入挖掘跨视图约束中隐藏的判别信息，而且能同时利用数据的结构信息。实验结果验证该方法的有效性。
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