摘要：

Real-world applications frequently involve multiple data modalities in the same samples, which are regarded as multi-view data. Multi-view clustering has been studied extensively in recent years to demonstrate embedded heterogeneity. However, most existing methods emphasize low-order correlation in multiple views, whereas approaches that incorporate high-order correlation are limited by the equal view-specific significance problem or a trade-off between global and local consistency. In this paper, we propose a co-regularized optimal graph-based clustering method known as Co-MSE, which integrates the correlation of different orders. By integrating the first-order and second-order similarities, the local structure is preserved, while an optimized embedding representation for multi-view data is obtained simultaneously through co-regularization. We demonstrate that Co-MSE can aid in providing a more suitable embedding representation and further enable satisfactory clustering performance. Extensive experiments on real-world datasets confirm the effectiveness and advantages of the proposed method.

论文发表截图：

作者介绍：

詹森文，2024年硕士毕业于中国人民大学数学学院。相关研究成果发表在Pattern Recognition、Bioinformatics、East Asian Journal on Applied Mathematics等国际期刊。

姜昊（通讯作者），中国人民大学数学学院教授，中国人民大学健康大数据研究院研究员，主要研究方向机器学习、数据挖掘、计算生物信息学、基于学习的建模、优化和控制等方面。

沈栋，中国人民大学数学学院教授，主要研究方向随机系统的学习控制与优化，机器学习及其在系统控制中的应用，分布式人工智能，随机逼近理论，启发式优化算法。