Multi-layer multi-level comprehensive learning for deep multi-view clustering

Multi-view clustering has attracted widespread attention because of its capability to identify the common semantics shared by the data captured from different views of data, objects or phenomena. This is a challenging problem but with the emergence of deep auto-encoder networks, the performance of multi-view clustering methods has considerably improved. However, it is notable that most existing methods merely utilize the features outputted by the last encoder layer to carry out the clustering task. Such approach neglects potentially useful information conveyed by the features of the previous layers. To address the this problem, we propose a novel multi-layer multi-level comprehensive learning framework for deep multi-view clustering (3MC). 3MC firstly conducts a contrastive learning involving different views based on deep features in each encoder layer separately, so as to achieve multi-view feature consistency. The next step is to construct layer-specific label MLPs to transform the features in each layer to high-level semantic labels. Finally, 3MC conducts an inter-layer contrastive learning using the high-level semantic labels in order to obtain multi-layer consistent clustering assignments. We demonstrate that the proposed comprehensive learning strategy, commencing from layer specific inter-view feature comparison to inter-layer high-level label comparison extracts and utilizes the underlying multi-view complementary information very successfully and achieves more accurate clustering. An extensive experimental comparison with the state-of-the-art methods demonstrates the effectiveness of the proposed framework. The code of this paper is available at https://github.com/chenzhe207/3MC.