基于张量图卷积的多视图聚类

Tensor Graph Convolution Networks for Multi-view Clustering

针对多视图聚类进行的数据表示学习,通常采用浅层模型与线性函数实现数据嵌入,该方式无法有效挖掘多种视图间丰富的数据关系.为充分表示不同视图间的一致性信息与互补性信息,本文提出基于张量图卷积的多视图聚类方法 (TGCNMC).该方法首先将传统的平面图拼接为张量图,并采用张量图卷积学习各视图中数据的近邻结构;接着利用图间卷积进行多视图间的信息传递,从而捕获多视图数据间的协同作用,揭示多视图数据中的一致性与互补性信息;最后采用自监督方式进行数据聚类.通过在标准数据集上进行的广泛实验,聚类效果优于现有的方法,表明该方法可以更全面的描述多视图数据、更有效地挖掘视图间的关系并具有更好的处理下游聚类任务的能力.

The shallow models and linear functions are usually utilized for data embedding in data representation learning aimed at multi-view clustering. This strategy, however, cannot effectively mine the rich data relationships among the multiple views. For better representation of the consistency and complementarity information among different views, a tensor graph convolution network for multi-view clustering(TGCNMC) is proposed in this study. This method splices the traditional plane graphs into tensor graphs and uses tensor graph convolution to learn the neighbor relationships of the data in each view. Then, inter-graph convolution is adopted to transfer information among multiple views and thereby to capture the synergistic effect among the data of multiple views and reveal the consistency and complementarity information in those data. Finally, the self-monitoring method is employed for data clustering. Extensive experiments are carried out on standard data sets and the corresponding clustering results are better than those of the existing methods,which indicates that this method can represent multi-view data comprehensively, mine the relationships among views effectively, and deal with downstream clustering tasks beneficially.