@Article{cmc.2026.087751,
AUTHOR = {Jiao Wang, Tingting Song, Yunhui Zhou},
TITLE = {Deep Incomplete Multi-View Clustering Based on Subspace Learning},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/cmc/online/detail/28332},
ISSN = {1546-2226},
ABSTRACT = {In practical scenarios, multi-view data often contains missing entries caused by complicated data collection and transmission procedures, posing great challenges to clustering analysis. Existing incomplete multi-view clustering methods have two obvious limitations: (1) Imputation-based methods inevitably generate inaccurate information during data recovery; (2) Imputation-free methods struggle to balance cross-view consistency and complementarity. To tackle the above issues, this paper proposes a deep incomplete multi-view clustering approach based on subspace learning, which integrates latent feature extraction, K-nearest neighbor-based feature imputation, cross-view contrastive alignment, and attention-driven fusion within a unified deep learning framework. Specifically, we first leverage autoencoders to extract view-specific latent features. Inspired by the observation that different views of the same sample should share similar latent representations, we impute missing latent features by leveraging cross-view correlations, using both view-specific complete features and common complete features through the K-nearest neighbor strategy. Then, we construct a fully-connected neural network layer to learn the self-expression matrix for each view. To minimize the impact of imputation noise on learning the self-expression matrix for clustering, these self-expression matrices are enforced to align using contrastive learning to capture cross-view consistency, and then fused based on a channel attention mechanism for pursuing cross-view complementarity. Finally, a high-quality cross-view consensus self-expression matrix is obtained for final clustering segmentation. Comprehensive evaluations on six benchmark datasets under various missing rates demonstrate the effectiveness and superiority of our method compared with state-of-the-art methods.},
DOI = {10.32604/cmc.2026.087751}
}