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Block-diagonal structure learning for subspace clustering
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AI
DOI：10.1016/j.eswa.2025.127767.png)
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Abstract
En 中文
Subspace clustering plays a pivotal role in discovering underlying structures within high-dimensional data by identifying clusters in lower-dimensional subspaces. However, existing works devote less attention to the potential contribution of the inherent structure to both similarity matrix construction and clustering structure identification. This paper aims to advance subspace clustering by investigating the block-diagonal structure of the similarity matrix. The core idea involves constructing a similarity matrix that assesses the similarities among high-dimensional data points, followed by a cluster-ordering procedure that establishes the necessary permutation. This facilitates the direct identification of clustering structures through the recognition of diagonal blocks in the permuted similarity matrix. Consequently, the main challenges involve permuting the similarity matrix to achieve the desired structure and efficiently identifying the diagonal blocks within the permuted similarity matrix. To address these challenges, we develop a traversal algorithm that detects high-density clusters in the dataset and generates an enhanced cluster ordering. The block-diagonal structure is achieved through permutation aligned with the traversal sequence. Additionally, we introduce a novel greedy segmentation algorithm to autonomously identify all diagonal blocks within the permuted similarity matrix, offering theoretical optimality. Experimental results demonstrate that the proposed method enhances the performance of state-of-the-art subspace clustering methods on synthetic and real datasets.
Keywords:
Subspace clustering
Block-diagonal
Similarity matrix
Density-based traversal
Journal
IF:
7.5
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2.9W
Citations:
10.2W