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.
翻译：常规多视角组群试图根据所有观点都得到充分观察的假设,将数据分成不同的组群。然而,在实际应用中,如疾病诊断、多媒体分析和建议系统,通常会发现,在许多情况下,并非所有样本都能看到,这导致常规多视角组群方法的失败。将这种不完整的多视角组群数据归为不完整的多视角组群。鉴于应用前景看好,对不完整的多视角组群的研究近年来取得了显著进展。然而,没有调查来总结目前的进展,并指出未来的研究方向。为此,我们审查最近关于不完整多视角组群的研究。重要的是,我们提供一些框架,以统一相应的不完整多视角组群集方法,并从理论和实验角度对某些具有代表性的方法进行深入的比较分析。最后,为研究人员提供了多视角组群群群群群群群群群群群群群群群群群群群群群群群群群群群群群群群群群集群集群集群集群集群集群集群集群集群集群集群集群集的一些未决问题。
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