@article{Wang2024, 
author = {Jiao Wang and Bin Wu and Hongying Zhang},
title = {Contrastive Consistency and Attentive Complementarity for Deep Multi-View Subspace Clustering},
year = {2024},
journal = {Computers, Materials & Continua},
volume = {79},
number = {1},
pages = {143-160},
keywords = {Deep multi-view subspace clustering, contrastive learning, adaptive fusion, self-expression learning},
url = {https://www.sciopen.com/article/10.32604/cmc.2023.046011},
doi = {10.32604/cmc.2023.046011},
abstract = {Deep multi-view subspace clustering (DMVSC) based on self-expression has attracted increasing attention due to its outstanding performance and nonlinear application. However, most existing methods neglect that view-private meaningless information or noise may interfere with the learning of self-expression, which may lead to the degeneration of clustering performance. In this paper, we propose a novel framework of Contrastive Consistency and Attentive Complementarity (CCAC) for DMVsSC. CCAC aligns all the self-expressions of multiple views and fuses them based on their discrimination, so that it can effectively explore consistent and complementary information for achieving precise clustering. Specifically, the view-specific self-expression is learned by a self-expression layer embedded into the auto-encoder network for each view. To guarantee consistency across views and reduce the effect of view-private information or noise, we align all the view-specific self-expressions by contrastive learning. The aligned self-expressions are assigned adaptive weights by channel attention mechanism according to their discrimination. Then they are fused by convolution kernel to obtain consensus self-expression with maximum complementarity of multiple views. Extensive experimental results on four benchmark datasets and one large-scale dataset of the CCAC method outperform other state-of-the-art methods, demonstrating its clustering effectiveness.}
}