Cross representation subspace learning for multi-view clustering

文献类型： 外文期刊

第一作者： Jiang, Hongwei

作者： Jiang, Hongwei;Ma, Wenming;Tong, Xiangrong;Wang, Yingjie;Du, Xiaolin;Jiang, Dalong;Luo, Yixuan;Zhang, Jinghui;Dai, Jian;Ding, Jianguo

作者机构：

关键词： Multi-view clustering; Latent representation; Tensor low-rank; Cross representation

期刊名称：EXPERT SYSTEMS WITH APPLICATIONS （ 影响因子：7.5； 五年影响因子：7.8 ）

ISSN： 0957-4174

年卷期： 2025 年 286 卷

页码：

收录情况： SCI

摘要： In recent years, there has been growing interest in multi-view subspace clustering for handling multiple-view data. While current methodologies typically aim to capture both consistency and complementarity between views, many approaches still struggle to fully exploit the potential of multiple-view data. Moreover, most existing methods operate directly in the raw space, overlooking the complex higher-order correlations across different views. To overcome the above issues, we have proposed a new approach named CRSL (Cross Representation Subspace Learning). This approach utilizes latent and subspace self-expressive representations to learn consistency and complementarity information simultaneously in multiple-view data. Additionally, we introduce a tensor structure to explore higher-order correlations between multiple views as a way to enhance the clustering results. The validity of the CRSL approach has been confirmed by experiments conducted on eight multiple-view data sets, demonstrating its advantages in dealing with multiple-view data and providing an effective approach for multi-view clustering.

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