Adaptive Graph Convolutional Subspace Clustering
This work addresses subspace clustering, a domain-specific problem in machine learning, with an incremental improvement over existing spectral-type algorithms.
The paper tackles the problem of subspace clustering by introducing an adaptive graph convolutional method that simultaneously improves feature extraction and coefficient matrix constraints, resulting in performance that surpasses related methods and some deep models.
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.