论文标题
多视图分层聚类
Multi-view Hierarchical Clustering
论文作者
论文摘要
本文着重于多视图聚类，该聚类旨在通过多视图数据促进聚类结果。通常，大多数现有作品都遭受参数选择和高计算复杂性的问题。为了克服这些局限性，我们提出了一个多视图层次聚类（MHC），该集群（MHC）在多个粒度层面上递归分配了多视图数据。具体而言，MHC由两个重要组成部分组成：余弦距离整合（CDI）和最近的邻居团（NNA）。 CDI可以探索多视图数据的基本互补信息，从而学习基本距离矩阵，该矩阵在NNA中用于获得聚类结果。值得注意的是，所提出的MHC可以轻松有效地用于无参数选择的实际应用中。九个基准数据集的实验说明了我们方法的优越性，与几种最先进的多视图聚类方法相比。
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.
