Incomplete multi-view clustering is a hot and emerging topic. It is well known that unavoidable data incompleteness greatly weakens the effective information of multi-view data. To date, existing incomplete multi-view clustering methods usually bypass unavailable views according to prior missing information, which is considered as a second-best scheme based on evasion. Other methods that attempt to recover missing information are mostly applicable to specific two-view datasets. To handle these problems, in this paper, we propose an information recovery-driven deep incomplete multi-view clustering network, termed as RecFormer. Concretely, a two-stage autoencoder network with the self-attention structure is built to synchronously extract high-level semantic representations of multiple views and recover the missing data. Besides, we develop a recurrent graph reconstruction mechanism that cleverly leverages the restored views to promote the representation learning and the further data reconstruction. Visualization of recovery results are given and sufficient experimental results confirm that our RecFormer has obvious advantages over other top methods.

翻译：不完整多视图聚类是一个热点且新兴的研究课题。众所周知，不可避免的数据不完整性会极大削弱多视图数据的有效信息。迄今为止，现有的不完整多视图聚类方法通常根据先验缺失信息规避不可用视图，这被视为一种基于规避的次优方案。其他尝试恢复缺失信息的方法大多仅适用于特定的两视图数据集。为解决这些问题，本文提出了一种面向信息恢复的深度不完整多视图聚类网络，命名为RecFormer。具体而言，我们构建了一个带有自注意力结构的两阶段自编码器网络，以同步提取多个视图的高层语义表征并恢复缺失数据。此外，我们开发了一种循环图重建机制，巧妙利用已恢复视图来促进表征学习及进一步的数据重建。恢复结果的可视化展示以及充分的实验结果表明，RecFormer相较于其他顶尖方法具有显著优势。