This paper focuses on the multi-view clustering, which aims to promote clustering results with multi-view data. Usually, most existing works suffer from the issues of parameter selection and high computational complexity. To overcome these limitations, we propose a Multi-view Hierarchical Clustering (MHC), which partitions multi-view data recursively at multiple levels of granularity. Specifically, MHC consists of two important components: the cosine distance integration (CDI) and the nearest neighbor agglomeration (NNA). The CDI can explore the underlying complementary information of multi-view data so as to learn an essential distance matrix, which is utilized in NNA to obtain the clustering results. Significantly, the proposed MHC can be easily and effectively employed in real-world applications without parameter selection. Experiments on nine benchmark datasets illustrate the superiority of our method comparing to several state-of-the-art multi-view clustering methods.
翻译:本文侧重于多视角组群,目的是用多视角数据促进组合结果。 通常,大多数现有工程都受到参数选择和高计算复杂性等问题的影响。为了克服这些限制,我们建议采用多视角等级分类组合(MHC),将多视角数据反复分布在多个颗粒层面。具体地说,MHC由两个重要部分组成:焦距整合(CDI)和近邻聚合(NNA)。 CDI可以探索多视角数据的基本补充信息,以便学习基本距离矩阵,该矩阵在NNA中用于获取组合结果。 重要的是,拟议的MHC可以在不选择参数的情况下在现实世界应用中容易和有效地使用。 对9个基准数据集的实验表明,我们的方法优于一些最先进的多视角组合方法。