Deep Double Self-Expressive Subspace Clustering
Ling Zhao Yunpeng Ma Shanxiong Chen Jun Zhou
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.