Abstract
The self-expressive coefficient plays a crucial role in the self-expressiveness-based subspace clustering method. To enhance the precision of the self-expressive coefficient, we propose a novel deep subspace clustering method, named grouping belief-based deep contrastive subspace clustering (GRESS), which integrates the clustering information and higher-order relationship into the coefficient matrix. Specifically, we develop a deep contrastive subspace clustering module to enhance the learning of both self-expressive coefficients and cluster representations simultaneously. This approach enables the derivation of relatively noiseless self-expressive similarities and cluster-based similarities. To enable interaction between these two types of similarities, we propose a unique grouping belief-based affinity refinement module. This module leverages grouping belief to uncover the higher-order relationships within the similarity matrix, and integrates the well-designed noisy similarity suppression and similarity increment regularization to eliminate redundant connections while complete absent information. Extensive experimental results on four benchmark datasets validate the superiority of our proposed method GRESS over several state-of-the-art methods.
Original language | English |
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Pages (from-to) | 148-160 |
Journal | IEEE Transactions on Cybernetics |
Volume | 55 |
Issue number | 1 |
Early online date | 21 Oct 2024 |
DOIs | |
Publication status | Published - Jan 2025 |
Bibliographical note
Publisher Copyright:© 2013 IEEE.
Keywords
- Deep subspace clustering
- grouping belief
- self-supervised learning