Adaptive Graph Convolutional Subspace Clustering
Lai Wei Zhengwei Chen Jun Yin Changming Zhu Rigui Zhou Jin Liu
Spectral-type subspace clustering algorithms have shown excellent performance
in many subspace clustering applications. The existing spectral-type subspace
clustering algorithms either focus on designing constraints for the
reconstruction coefficient matrix or feature extraction methods for finding
latent features of original data samples. In this paper, inspired by graph
convolutional networks, we use the graph convolution technique to develop a
feature extraction method and a coefficient matrix constraint simultaneously.
And the graph-convolutional operator is updated iteratively and adaptively in
our proposed algorithm. Hence, we call the proposed method adaptive graph
convolutional subspace clustering (AGCSC). We claim that by using AGCSC, the
aggregated feature representation of original data samples is suitable for
subspace clustering, and the coefficient matrix could reveal the subspace
structure of the original data set more faithfully. Finally, plenty of subspace
clustering experiments prove our conclusions and show that AGCSC outperforms
some related methods as well as some deep models.