Multi-view clustering aims to group multi-view samples into different clusters based on the similarity. Since side information can describe the relation between samples, for example, must-links and cannot-links, thus multi-view clustering with the consideration about side information along with samples can get more feasible clustering results. As a recent developed multi-view clustering approach, multi-view matrix completion (MVMC) constructs similarity matrix for each view and casts clustering into a matrix completion problem. Different from traditional multi-view clustering approaches, MVMC enforces the consistency of clustering results on different views as constraints for alternative optimization and the global optimal solution can be obtained. Although related experiments show that MVMC exhibits impressive performance, it still neglects the possibility of a sample belonging to a cluster. In this paper, we consider the possibility on the base of entropy and develop an entropy-based multi-view matrix completion for clustering with side information (EMVMC). Experiments on multi-view datasets Course, Citeseer, Cora, WebKB, NewsGroup, and Reuters validate the effectiveness of EMVMC.

中文翻译：

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基于熵的多视角矩阵完成与边信息的聚类

多视图聚类旨在基于相似性将多视图样本分组为不同的聚类。由于辅助信息可以描述样本之间的关系，例如必须链接和不能链接，因此考虑到辅助信息以及样本的多视图聚类可以获得更可行的聚类结果。作为最近开发的多视图聚类方法，多视图矩阵完成（MVMC）为每个视图构造相似性矩阵，并将聚类转换为矩阵完成问题。与传统的多视图聚类方法不同，MVMC会在不同视图上强制执行聚类结果的一致性，这是替代优化的约束条件，并且可以获得全局最优解。尽管相关实验表明MVMC具有出色的性能，它仍然忽略了样本属于簇的可能性。在本文中，我们考虑了基于熵的可能性，并开发了基于熵的多视图矩阵完成方法，用于与边信息进行聚类（EMVMC）。在多视图数据集Course，Citeseer，Cora，WebKB，NewsGroup和Reuters上进行的实验验证了EMVMC的有效性。