Deep Double Self-Expressive Subspace Clustering
This work addresses an incremental improvement in subspace clustering for machine learning researchers, focusing on enhancing performance in specific datasets.
The paper tackles the problem of limited clustering performance in deep subspace clustering by proposing a double self-expressive algorithm that reduces subspace-preserving representation error and improves connectivity, achieving better clustering than state-of-the-art methods on benchmark datasets.
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.