In this paper, we propose a general framework for incomplete multiview clustering. The proposed method is the first work that exploits the graph learning and spectral clustering techniques to learn the common representation for incomplete multiview clustering. First, owing to the good performance of low-rank representation in discovering the intrinsic subspace structure of data, we adopt it to adaptively construct the graph of each view. Second, a spectral constraint is used to achieve the low-dimensional representation of each view based on the spectral clustering. Third, we further introduce a co-regularization term to learn the common representation of samples for all views, and then use the ${k}$ -means to partition the data into their respective groups. An efficient iterative algorithm is provided to optimize the model. Experimental results conducted on seven incomplete multiview datasets show that the proposed method achieves the best performance in comparison with some state-of-the-art methods, which proves the effectiveness of the proposed method in incomplete multiview clustering.

中文翻译：

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具有自适应图学习的不完整多视图谱聚类

在本文中，我们提出了一个不完整的多视图聚类的通用框架。该方法是利用图学习和谱聚类技术来学习不完整多视图聚类的通用表示形式的第一项工作。首先，由于低秩表示法在发现数据的内在子空间结构方面具有良好的性能，因此我们采用它来自适应地构造每个视图的图。其次，基于光谱聚类，使用光谱约束来实现每个视图的低维表示。第三，我们进一步引入一个共正则化术语，以学习所有视图的样本的通用表示形式，然后使用$ {k} $ -means将数据划分为各自的组。提供了一种有效的迭代算法来优化模型。