论文标题
一项关于不完整的多视图集群的调查
A Survey on Incomplete Multi-view Clustering
论文作者
论文摘要
传统的多视图聚类试图基于所有观点的假设，以完全观察到所有观点的假设。但是，在诸如疾病诊断，多媒体分析和建议系统之类的实际应用中，常见的是，并非所有样本观点在许多情况下都可以使用，这会导致常规多视图聚类方法的失败。在此不完整的多视图数据上的聚类称为不完整的多视图聚类。鉴于有希望的应用前景，近年来对不完整的多视图聚类的研究取得了明显的进步。但是，没有调查可以总结当前的进展并指出未来的研究方向。为此，我们回顾了最近关于不完整的多视图聚类的研究。重要的是，我们提供一些框架来统一相应的不完整的多视图聚类方法，并从理论和实验角度对某些代表性方法进行深入的比较分析。最后，为研究人员提供了不完整的多视图聚类领域中的一些开放问题。
Conventional multi-view clustering seeks to partition data into respective groups based on the assumption that all views are fully observed. However, in practical applications, such as disease diagnosis, multimedia analysis, and recommendation system, it is common to observe that not all views of samples are available in many cases, which leads to the failure of the conventional multi-view clustering methods. Clustering on such incomplete multi-view data is referred to as incomplete multi-view clustering. In view of the promising application prospects, the research of incomplete multi-view clustering has noticeable advances in recent years. However, there is no survey to summarize the current progresses and point out the future research directions. To this end, we review the recent studies of incomplete multi-view clustering. Importantly, we provide some frameworks to unify the corresponding incomplete multi-view clustering methods, and make an in-depth comparative analysis for some representative methods from theoretical and experimental perspectives. Finally, some open problems in the incomplete multi-view clustering field are offered for researchers.
