Federated Incomplete Multi-view Clustering with Individual Structure Preservation and Central Representation Tensorization

Data absence and privacy preservation are critical concerns in exploring the data clustering structure. Anchor-based incomplete multi-view clustering methods can efficiently reveal the intrinsic structure of heterogeneous data and has attracted plenty of attentions in recent years. However, current researches face two problems: the incomplete samples leads to structural representation discrepancies, and a single structure cannot implement effective data mining; data is typically stored in a distributed manner, and the consequent privacy requirement imposes difficulties for model optimization with consistent representation. In this study, we propose a unified anchor-based incomplete multi-view clustering method in federated learning framework for distributed data, revolving around individual structure preservation and server-central tensor regularization. The framework designs an adaptive embedding graph learning strategy to dynamically capture global and local structures within clients. A tensor regularization term is developed, which explores higher-order correlations across clients with adaptive weights, and further guides the complementarity of client-specific information. Moreover, we construct the optimization framework combined with the augmented Lagrangian method to both reduce the complexity and prevent the leakage of data privacy. Experimental studies are compared with the state-of-the-art algorithms to demonstrate that the proposed method implements effective incomplete data exploration, strengthening the applicability in distributed environment for IMVC research. The codes of this article are released in https://github.com/LiYannnnnudt/FIMC.