Deep Multi-view Subspace Clustering Based on Adaptive Weight Fusion

In view of the inability of deep multi-view subspace clustering network to distinguish the reliability of each view when data fusion, and the lack of utilization of the consistent and complementary information between multi-view data, a deep multi-view subspace clustering method based on adaptive weight fusion (DMSC-AWF) was proposed. First, a common representation matrix was studied by making each view of sharing the same self-representation layer, and a self-representation layer was built for each visual to learn a specific representation matrix. The efficient use of consistent and complementary information that depends on the data was ensured. Second, the importance of different views was quantified by introducing attention modules based on the shared self-representation layer, which adaptively assigned weights to each visual data. Finally, clustering experiments were conducted on four public datasets, and the clustering results of this method were significantly improved compared with the comparison method. Moreover, the validity and importance of the attention module learning visual weight were verified by the degradation experiment.