融合一致性和多样性的自适应加权多视图聚类

doi:10.3778/j.issn.1002-8331.2408-0004

摘要/Abstract

摘要： 多视图聚类能够充分融合多个视图的信息，从而表现出优秀的聚类性能。然而，现有方法大多只关注视图间的一致性信息，忽略了视图间的多样性信息，并且在秩的近似估计准确性方面存在不足，从而影响了算法的效果。为了解决这一问题，提出了融合一致性和多样性的自适应加权多视图聚类。该算法为每个视图构建初始相似图，引入张量对数行列式项最大化逼近秩的真实值。该算法通过多样性项探索视图内和视图间的多样性信息，并采用自适应加权图融合项提取每个视图的一致性信息；通过不断地迭代优化，最终得到一个高质量融合图。在八个真实数据集上的实验结果表明，所提方法明显优于基线方法。

关键词: 多视图聚类, 一致性信息, 多样性信息, 自适应加权

Abstract: Multi-view clustering exhibits excellent clustering performance because it can fully integrate information from multiple views. However, most of the existing methods focus only on the consensus information among views, while ignoring the diversity information among views and lacking the accuracy of approximation rank, which ultimately affects the effectiveness of the clustering results. To handle this issue, a multi-view clustering algorithm based on the tensor log-determinant, named auto-weighted multi-view clustering incorporating consensus and diversity is proposed. Specifically, this algorithm first constructs an initial similarity graph for each view, and then uses the tensor log-determinant to maximally approximate the true value of the rank. Subsequently, the algorithm explores intra-view and inter-view diversity information by using a diversity term, and uses an adaptive weighted graph fusion term to extract consensus information from each view. Through iterative optimization, a high-quality fusion graph is finally obtained. Experimental results on eight real-world datasets show that the proposed method significantly outperforms state-of-the-art baselines.

Key words: multi-view clustering, consensus information, diversity information, adaptive weighting

姚怡莹, 陈梅, 王洁, 郭爱霞. 融合一致性和多样性的自适应加权多视图聚类[J]. 计算机工程与应用, 2025, 61(23): 135-148.

YAO Yiying, CHEN Mei, WANG Jie, GUO Aixia. Auto-Weighted Multi-View Clustering Incorporating Consensus and Diversity[J]. Computer Engineering and Applications, 2025, 61(23): 135-148.

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