Federated Multi-View Clustering via Tensor Factorization

Federated Multi-View Clustering via Tensor Factorization

Wei Feng, Zhenwei Wu, Qianqian Wang, Bo Dong, Zhiqiang Tao, Quanxue Gao

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Main Track. Pages 3962-3970. https://doi.org/10.24963/ijcai.2024/438

Multi-view clustering is an effective method to process massive unlabeled multi-view data. Since data of different views may be collected and held by different parties, it becomes impractical to train a multi-view clustering model in a centralized way, for the sake of privacy. However, federated multi-view clustering is challenging because multi-view learning has to consider the complementary and consistent information between each view distributed across different clients. For another, efficiency is highly expected in federated scenarios. Therefore, we propose a novel federated multi-view clustering method with tensor factorization (TensorFMVC), which is built based on K-means and hence is more efficient. Besides, TensorFMVC avoids initializing centroids to address the performance degradation of K-means due to its sensitivity to centroid initialization. A three-order tensor stacked by cluster assignment matrices is introduced to exploit the complementary information and spatial structure of different views. Furthermore, we divide the optimization into several subproblems and develop a federated optimization approach to support cooperative model training. Extensive experiments on several datasets demonstrate that our proposed method exhibits superior performance in federated multi-view clustering.
Keywords:
Machine Learning: ML: Federated learning
Machine Learning: ML: Multi-view learning