Diversity-induced Multi-view Subspace Clustering Algorithm with Grouping Effect

doi:10.19678/j.issn.1000-3428.0070348

Abstract

Abstract:

The multi-view subspace clustering algorithm, a type of multi-view clustering algorithm, emphasizes discovering potential subspaces in multi-view data and clustering based on these subspaces. The Multi-view Subspace Clustering algorithm with Grouping Effects (MvSCGE) is also a type of multi-view subspace clustering algorithm. The basic principle is to learn the subspace representation of each view through smooth regularization, while ensuring cross view consistency, and ultimately learning a consistent clustering index matrix. The clustering results are obtained after processing. However, this algorithm only considers the local structure of a single view and has certain limitations. To further explore the diversity between views, this paper proposes a Diversity-induced Multi-view Subspace Clustering algorithm with Grouping Effect (DiMvSCGE). It preserves the local structure of each view while using the Hilbert-Schmidt Independence Criterion (HSIC) to measure the diversity between views, and iteratively uses alternating direction minimization on this basis. Based on the clustering index matrix obtained after the iterations, k-means clustering is performed to obtain the final result. Comparative experiments with several advanced algorithms on four public datasets show that this algorithm offers advantages, such as low parameter sensitivity and fast convergence speed, and demonstrates good performance on different datasets.

Key words: multiple views, view grouping effect, subspace clustering, diversity induction, principle of complementarity

摘要：

多视图子空间聚类算法作为多视图聚类算法的一种, 强调在多视图数据中发现潜在的子空间, 从而基于子空间进行聚类。具有分组效应的多视图子空间聚类算法(MvSCGE)是一种多视图子空间聚类算法, 主要思想为通过光滑正则化来学习每个视图的子空间表示, 同时保证跨视图一致性, 并最终学习到一致的聚类指标矩阵, 处理后得出聚类结果。但是, 该算法只考虑到单个视图的局部结构, 仍存在一定局限性。为进一步挖掘视图间的多样性, 提出一种具有分组效应的多样性诱导多视图子空间聚类算法(DiMvSCGE)。该算法在保留每个视图局部结构的同时, 利用希尔伯特-施密特独立准则(HSIC)来衡量视图间的多样性, 并在此基础上使用交替方向最小化进行迭代, 在迭代后获得的聚类指标矩阵基础上, 进行k均值聚类, 得到最终的结果。在4个公共数据集上与几种先进算法的对比实验证明, 该算法拥有参数敏感度低、收敛速度快等优势, 且在不同数据集上都表现出了良好的性能。

关键词: 多视图, 视图分组效应, 子空间聚类, 多样性诱导, 互补性原则

ZHANG Yuechen, GE Hongwei, LI Ting. Diversity-induced Multi-view Subspace Clustering Algorithm with Grouping Effect[J]. Computer Engineering, 2026, 52(6): 96-108.

张悦辰, 葛洪伟, 李婷. 具有分组效应的多样性诱导多视图子空间聚类算法[J]. 计算机工程, 2026, 52(6): 96-108.

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Figures/Tables 11

Fig.1 Structure diagram of DiMvSCGE algorithm

Fig.2 Comparison of adjacency matrix visualization graphs on MSRC-v5 dataset

Fig.3 Comparison of adjacency matrix visualization graphs on BBCSport dataset

Fig.4 Scatter plot comparison of clustering results on MSRC-v5 dataset

Fig.5 Scatter plot comparison of clustering results on 3Sources dataset

Fig.6 Convergence curves of DiMvSCGE on four benchmark datasets

Fig.7 Clustering results of different parameter values on 3Sources dataset

Fig.8 Clustering results of different parameter values on BBCSport dataset

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