Multi-view Self-Expressive Subspace Clustering Network

Advanced deep multi-view subspace clustering methods are based on the self-expressive model, which has achieved impressive performance. However, most existing works have several limitations: 1) They endure high computational complexity when learning a consistent affinity matrix, impeding their capacity to handle large-scale multi-view data; 2) The global and local structure information of multi-view data remains under-explored. To tackle these challenges, we propose a simplistic but holistic framework called Multi-view Self-Expressive Subspace Clustering (MSESC) network. Specifically, we design a deep metric network to replace the conventional self-expressive model, which can directly and efficiently produce the intrinsic similarity values of any instance-pairs of all views. Moreover, our method explores global and local structure information from the connectivity of instance-pairs across views and the nearest neighbors of instance-pairs within the view, respectively. By integrating global and local structure information within a unified framework, MSESC can learn a high-quality shared affinity matrix for better clustering performance. Extensive experimental results indicate the superiority of MSESC compared to several state-of-the-art methods.