In order to reduce the negative effect of missing data on clustering, incomplete multiview clustering (IMVC) has become an important research content in machine learning. At present, graph-based methods are widely used in IMVC, but these methods still have some defects. First, some of the methods overlook potential relationships across views. Second, most of the methods depend on local structure information and ignore the global structure information. Third, most of the methods cannot use both global structure information and potential information across views to adaptively recover the incomplete relationship structure. To address the above issues, we propose a unified optimization framework to learn reasonable affinity relationships, called low-rank graph completion-based IMVC (LRGR_IMVC). 1) Our method introduces adaptive graph embedding to effectively explore the potential relationship among views; 2) we append a low-rank constraint to adequately exploit the global structure information among views; and 3) this method unites related information within views, potential information across views, and global structure information to adaptively recover the incomplete graph structure and obtain complete affinity relationships. Experimental results on several commonly used datasets show that the proposed method achieves better clustering performance significantly than some of the most advanced methods.

中文翻译：

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基于低秩图完成的不完全多视图聚类。

为了减少缺失数据对聚类的负面影响，不完全多视图聚类（IMVC）已成为机器学习中的重要研究内容。目前，基于图的方法在IMVC中得到广泛应用，但这些方法仍存在一些缺陷。首先，一些方法忽略了视图之间的潜在关系。其次，大多数方法都依赖于局部结构信息而忽略了全局结构信息。第三，大多数方法不能同时使用全局结构信息和跨视图的潜在信息来自适应地恢复不完整的关系结构。为了解决上述问题，我们提出了一个统一的优化框架来学习合理的亲和关系，称为基于低秩图完成的 IMVC (LRGR_IMVC)。1）我们的方法引入了自适应图嵌入来有效地探索视图之间的潜在关系；2）我们附加了一个低秩约束以充分利用视图之间的全局结构信息；3）该方法联合视图内的相关信息、跨视图的潜在信息和全局结构信息自适应地恢复不完整的图结构并获得完整的亲和关系。在几个常用数据集上的实验结果表明，与一些最先进的方法相比，所提出的方法显着实现了更好的聚类性能。跨视图的潜在信息和全局结构信息自适应地恢复不完整的图结构并获得完整的亲和关系。在几个常用数据集上的实验结果表明，与一些最先进的方法相比，所提出的方法显着实现了更好的聚类性能。跨视图的潜在信息和全局结构信息自适应地恢复不完整的图结构并获得完整的亲和关系。在几个常用数据集上的实验结果表明，与一些最先进的方法相比，所提出的方法显着实现了更好的聚类性能。