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新兴的热点研究课题。众所周知，不可避免的数据不完整性会大幅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跳过不可用视图，这被视为一种基于规避的次优方案。而其他试图恢复缺失信息的方法大多仅适用于特定的双视图数据集。为解决这些问题，本文提出了一种信息恢复驱动的深度不完整多视图聚类网络，称为RecFormer。具体而言，我们构建了一个具有自注意力结构的两阶段自编码器网络，用于同步提取多个视图的高层语义表示并恢复缺失数据。此外，我们开发了一种递归图重构机制，巧妙利用已恢复视图促进表示学习及进一步的数据重构。给出了恢复结果的可视化，充分的实验结果表明，我们的RecForme与其他最优方法相比具有显著优势。