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GRESS: Grouping Belief-Based Deep Contrastive Subspace Clustering
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AI
DOI：10.1109/TCYB.2024.3475034.png)
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Abstract
En 中文
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.
Keywords:
Contrastive learning
Clustering methods
Vectors
Representation learning
Noise measurement
Feature extraction
Aerospace electronics
Sparse matrices
Decoding
Cybernetics
Deep subspace clustering
grouping belief
self-supervised learning
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W
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