TY  - EJOU
AU  - Wang, Jiao 
AU  - Song, Tingting 
AU  - Zhou, Yunhui 

TI  - Deep Incomplete Multi-View Clustering Based on Subspace Learning
T2  - Computers, Materials \& Continua

PY  - 
VL  - 
IS  - 
SN  - 1546-2226

AB  - 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.
KW  - Deep incomplete multi-view clustering; subspace learning; missing feature imputation; contrastive alignment; attentive fusion

DO  - 10.32604/cmc.2026.087751