@Article{cmc.2023.046011,
AUTHOR = {Jiao Wang, Bin Wu, Hongying Zhang},
TITLE = {Contrastive Consistency and Attentive Complementarity for Deep Multi-View Subspace Clustering},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {79},
YEAR = {2024},
NUMBER = {1},
PAGES = {143--160},
URL = {http://www.techscience.com/cmc/v79n1/56259},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2023.046011}
}