发表机构
Dalian University of Technology; Inspur Group Co., Ltd.; China University of Mining and Technology; The University of Warwick（大连理工大学; 浪潮集团有限公司; 中国矿业大学; 华威大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对多视图聚类中的部分视图对齐问题，提出DAS-PMVC框架，通过锚点图结构对齐、结构增强特征学习与双对齐策略提升性能，在多数据集上优于现有最先进方法。
AI 中文摘要
近年来，多视图聚类已吸引广泛研究关注。然而，由于数据采集设备的限制，不同视图的数据常存在不对齐问题，导致部分视图对齐问题（PVAP）。为缓解视图不对称与无关样本的影响，本文提出一种通过双对齐与结构增强实现的部分多视图聚类框架（DAS-PMVC），该框架利用视图结构一致性与语义相关性。具体而言，DAS-PMVC包含三个部分：一是锚点图结构对齐，其中基于锚点关系推导具有一致潜在空间的样本联合嵌入表示，以实现初始视图对齐；二是结构增强特征学习，模型通过预训练学习视图结构信息，并结合多视图图卷积网络从对齐的图结构中进一步提取深度潜在特征，以提升表示的判别能力；三是双对齐策略，在预训练阶段通过锚点图执行初始对齐，在训练阶段引入对比学习损失与匈牙利算法，进一步优化潜在特征的对齐。在多个数据集上的实验结果表明，DAS-PMVC框架在聚类性能上优于现有最先进的方法，展现出其有效性与优越性。
英文摘要
In recent years, multi-view clustering has attracted widespread research interest. However, due to limitations in data collection devices, data across different views often suffer from misalignment, leading to the partial view alignment problem (PVAP). To mitigate the impact of view asymmetry and irrelevant samples, this paper proposes a framework for partial multi-view clustering via dual alignment and structure enhancement (DAS-PMVC), which leverages view structure consistency and semantic relevance. Specifically, DAS-PMVC includes three parts: \textbf{anchor graph structure alignment}, where sample joint embedding representations with consistent latent space are derived from anchor point relationships for initial view alignment; \textbf{structure-enhanced feature learning}, where the model learns view structure information through pretraining and combines multi-view graph convolutional networks to further extract deep latent features from the aligned graph structure to improve the discriminative power of representations; and \textbf{a dual alignment strategy}, where initial alignment is performed through the anchor graph in the pretraining phase, and contrastive learning loss and the Hungarian algorithm are introduced in the training phase to further optimize the alignment of latent features. Experimental results on various datasets demonstrate that the DAS-PMVC framework outperforms existing state-of-the-art methods in clustering performance, showcasing its effectiveness and superiority.
Comments8 pages, 4 figures. Accepted by ACM Multimedia 2026
