Deep subspace clustering based on auto-encoder has received wide attention.
However, most subspace clustering based on auto-encoder does not utilize the
structural information in the self-expressive coefficient matrix, which limits
the clustering performance. In this paper, we propose a double self-expressive
subspace clustering algorithm. The key idea of our solution is to view the
self-expressive coefficient as a feature representation of the example to get
another coefficient matrix. Then, we use the two coefficient matrices to
construct the affinity matrix for spectral clustering. We find that it can
reduce the subspace-preserving representation error and improve connectivity.
To further enhance the clustering performance, we proposed a self-supervised
module based on contrastive learning, which can further improve the performance
of the trained network. Experiments on several benchmark datasets demonstrate
that the proposed algorithm can achieve better clustering than state-of-the-art
methods.

本文提出了一种双自表达子空间聚类算法，利用自表达系数构建相似度矩阵进行谱聚类，并提出了基于对比学习的自监督模块，该算法在多个基准数据集上实现了比现有方法更好的聚类表现。