Consensus multi-view spectral clustering network with unified similarity

The learning of a consensus representation is critical to multi-view spectral clustering, which generally involves two stages: affinity matrix construction from heterogeneous views and consensus embedding learning for clustering. However, most existing methods construct affinity matrices separately for each view, which hinders the learning of a unified similarity across views. Furthermore, limited attention has been paid to explicitly enforcing consistency among embedding representations, often resulting in suboptimal clustering performance. To address these issues, we propose an effective deep multi-view spectral clustering network to learn consensus representation. Specifically, we first propose a deep spectral embedding learning framework with unified similarity. This framework jointly integrates data to learn a unified similarity across multiple views, and further constructs a spectral mapping network to extract common embedding representations. To further learn sufficient consensus information, we align spectral embedding representations across different views using local structure-constrained contrastive learning. Different samples with high local similarity have their spectral embeddings constrained to be consistent, thus improving the clustering performance. Comparative experiments on eight public datasets validate the superiority and effectiveness of the proposed algorithm.