Semi-supervised multi-view learning has recently achieved appealing performance with the consensus relation between samples. However, in addition to the relation between samples, the relation between samples and their assemble centroid is also important to the learning. In this paper, we propose a novel model based on orthogonal non-negative matrix factorization, which allows exploring both the consensus relations between samples and between samples and their assemble centroid. Since this model utilizes more consensus information to guide the multi-view learning, it can lead to better performance. Meanwhile, we theoretically derive a proposition about the equivalency between the partial orthogonality and the full orthogonality. Based on this proposition, the orthogonality constraint and the label constraint are simultaneously implemented in the proposed model. Experimental evaluations on five real-world datasets show that our approach outperforms the state-of-the-art methods, where the improvement is 6% average in terms of ARI index.

半监督多视图学习最近通过样本之间的一致性关系取得了吸引人的性能。然而，除了样本之间的关系外，样本与其组装质心之间的关系对学习也很重要。在本文中，我们提出了一种基于正交非负矩阵分解的新模型，该模型允许探索样本之间以及样本之间及其组装质心之间的一致性关系。由于该模型利用更多的共识信息来指导多视图学习，因此可以带来更好的性能。同时，我们从理论上推导出了一个关于部分正交性和完全正交性之间等价性的命题。基于这个提议，正交性约束和标签约束在所提出的模型中同时实现。对五个真实世界数据集的实验评估表明，我们的方法优于最先进的方法，其中 ARI 指数的平均改进为 6%。