Multiview clustering has gained increasing attention recently due to its ability to deal with multiple sources (views) data and explore complementary information between different views. Among various methods, multiview subspace clustering methods provide encouraging performance. They mainly integrate the multiview information in the space where the data points lie. Hence, their performance may be deteriorated because of noises existing in each individual view or inconsistent between heterogeneous features. For multiview clustering, the basic premise is that there exists a shared partition among all views. Therefore, the natural space for multiview clustering should be all partitions. Orthogonal to existing methods, we propose to fuse multiview information in partition level following two intuitive assumptions: (i) each partition is a perturbation of the consensus clustering; (ii) the partition that is close to the consensus clustering should be assigned a large weight. Finally, we propose a unified multiview subspace clustering model which incorporates the graph learning from each view, the generation of basic partitions, and the fusion of consensus partition. These three components are seamlessly integrated and can be iteratively boosted by each other towards an overall optimal solution. Experiments on four benchmark datasets demonstrate the efficacy of our approach against the state-of-the-art techniques.

中文翻译：

---

分区级多视图子空间聚类。

由于多视图聚类能够处理多个源（视图）数据并探索不同视图之间的补充信息，因此近来受到越来越多的关注。在各种方法中，多视图子空间聚类方法可提供令人鼓舞的性能。它们主要将多视图信息集成在数据点所在的空间中。因此，由于每个单独视图中存在的噪声或异构特征之间的不一致，它们的性能可能会降低。对于多视图群集，基本前提是所有视图之间存在共享分区。因此，用于多视图群集的自然空间应该是所有分区。与现有方法正交，我们建议按照两个直观的假设在分区级别融合多视图信息：（i）每个分区都是共识集群的扰动；（ii）应该为接近共识聚类的分区分配较大的权重。最后，我们提出了一个统一的多视图子空间聚类模型，该模型结合了从每个视图进行图学习，基本分区的生成以及共识分区的融合。这三个组件无缝地集成在一起，并且可以相互迭代地提升为一个整体的最佳解决方案。在四个基准数据集上进行的实验证明了我们的方法相对于最新技术的有效性。基本分区的生成，以及共识分区的融合。这三个组件无缝地集成在一起，并且可以相互迭代地提升为一个整体的最佳解决方案。在四个基准数据集上进行的实验证明了我们的方法相对于最新技术的有效性。基本分区的生成，以及共识分区的融合。这三个组件无缝地集成在一起，并且可以相互迭代地提升为一个整体的最佳解决方案。在四个基准数据集上进行的实验证明了我们的方法相对于最新技术的有效性。