Balanced Multi-View Clustering

doi:10.1109/tpami.2026.3688728

2026-04-29

PRE

Xinwang Liu (Xinwang Liu)

DOI：10.1109/tpami.2026.3688728

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摘要

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多视角聚类（MvC）旨在整合不同视角的信息以增强模型捕捉潜在数据结构的能力。MvC中广泛使用的联合训练范式由于对所有视角采用统一的训练目标，可能导致视角特定特征不平衡且未充分优化，从而未能充分利用多视角信息。例如，在联合训练范式中，包含更多判别信息的特定视角可能主导学习过程，导致其他视角优化不足。为缓解此问题，我们首先从各视角特定特征提取器的梯度下降角度分析了联合训练范式中多视角聚类的不平衡现象。然后，提出了一种新颖的平衡多视角聚类（BMvC）方法，引入视角特定对比正则化（VCR）来调节各视角的优化。具体而言，VCR将联合特征和视角特定特征中捕捉到的样本相似性融入视角特定特征的聚类分布中，以增强视角特定特征提取器的学习过程。此外，分析表明VCR能自适应地调节梯度幅度以更新视角特定特征提取器的参数，实现平衡的多视角学习过程。通过这种方式，BMvC在利用视角特定模式和探索视角不变模式之间取得了更好的权衡，从而充分学习聚类任务的多视角信息。最后，在八个基准MvC数据集和两个空间分辨转录组学数据集上进行了实验，验证了所提方法相比当前最优方法的优越性。

Keyword:

Balanced learning

contrastive learning

multi-view clustering

multi-view learning

view cooperation

期刊

IEEE Transactions on Pattern Analysis and Machine Intelligence

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18.6

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1.1K

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qilu university of technology (shandong academy of sciences)

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835

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national university of defense technology

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Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences

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100

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huazhong university of science and technology

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2.7W

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被引数: 5

zhejiang normal university

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3.2K

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