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Projection-based comprehensive multi-view clustering with smooth regularization

delete2025-05-01
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PRE
AI
X
Xiaoqian Zhang
李
李景浩 (Jinghao Li) *
S
Shuai Zhao
郑一露 封面图
郑一露 (Yilu Zheng)
Y
Yufeng Chen
DOI：10.1016/j.asoc.2025.113025delete
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摘要

摘要

En 中文
通过挖掘数据的潜在表示，子空间聚类方法可以更加准确和鲁棒。然而，这类方法存在以下局限性：它们在构建低维潜在表示时，缺乏显式保留原始数据簇中个体间关系的能力。具体而言，在数据降维过程中，无法确保保留的信息是样本间的相关信息。其次，数据降维可能导致部分样本的关键信息丢失，影响自表示矩阵的构建并降低聚类性能。为解决这两个问题，提出了一种新的多视角聚类方法，即基于投影的综合多视角聚类与平滑正则化（PCMCS）。我们为投影矩阵设计了平滑正则项，使降维后的数据能保留原始数据的分组效果。然后，我们捕捉原始数据及降维后数据的表示矩阵，并将所得表示矩阵构建为张量，使两个自表示矩阵相互优化，这在一定程度上中和了信息丢失、数据冗余和噪声对表示矩阵构建的干扰，提升了聚类性能。在8个数据集上进行的实验证明了PCMCS的有效性。
Keyword:
Latent representation
Multi-view clustering
Projection
Grouping effect
Tensor

期刊

Applied Soft Computing 封面图
Applied Soft Computing
IF:
6.6
论文数:
1.4W
被引数:
4.8W

机构

S
Southwest University of Science and Technology
学者数:
3.2K
论文数: 1.0K
被引数: 10.0K
U
University of Science and Technology of China
学者数:
1.7W
论文数: 6.0K
被引数: 11.3W
引用论文

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