Deep Low-Rank Multi-View Subspace Clustering

To deal with the problem that the current deep multi-view subspace clustering methods lack robustness due to the lack of low-rank representation constraints on the self-representation matrix, we propose a novel deep low-rank multi-view subspace clustering (DLRMSC) method. Based on the deep multi-view subspace clustering algorithm, the self-expression layer is split into two low-rank linear self-expression layers: the commonly shared self-expression layer and the view-specific self-expression layer, which becomes a double-layer self-expression module with low-rank constraint. To make full use of the complementarity of multi-view data, the parameters of the commonly shared self-expression layer are forced to be the same, but the parameters of specific self-expression layer are different. We embed the self-expression module in the middle of the deep auto-encoder of each view, so as to obtain a deep low-rank multi-view subspace clustering model which can be solved by backpropagation. In the training stage, the commonly shared self-expression layer extracts the common information of multi-view data, the view-specific self-expression layer extracts the specific information of each view, and the double-layer self-expression module guarantees low-rank representation constraint. The experimental results on six public datasets show that compared with the deep multi-view subspace clustering algorithm, the clustering accuracy and normalized mutual information (NMI) of this proposed method on six datasets are significantly improved, where the average accuracy is increased by 0.064 and the average NMI is increased by 0.064. The DLRMSC method substantially outperforms the eleven comparative state-of-the-art clustering methods, and its accuracy is increased by 0.097 and NMI is increased by 0.103 at most. © 2021, Editorial Office of Journal of Xi'an Jiaotong University. All right reserved.