2024/12/31 by Yupei Zhang, Zhang, Yupei, Ruojia Feng +8
Computer Science · #Advanced Clustering Algorithms Research #Artificial intelligence #Cluster analysis #Computer science #Data mining #Face and Expression Recognition #Neural Networks and Applications #Pattern recognition (psychology) #Subspace topology
paper · pdf · doi:10.1007/s11390-025-5304-4
published in Journal of Computer Science and Technology 41(2), 609-620 (Springer Science+Business Media)
openalex created_date 2025/01/04 · openalex publication_date 2026/03/01 · openalex updated_date 2026/07/28
This paper introduces FDSC, a private-protected subspace clustering (SC) approach with federated learning (FC) schema. In each client, there is a deep subspace clustering network accounting for grouping the isolated data, composed of a encode network, a self-expressive layer, and a decode network. FDSC is achieved by uploading the encode network to communicate with other clients in the server. Besides, FDSC is also enhanced by preserving the local neighborhood relationship in each client. With the effects of federated learning and locality preservation, the learned data features from the encoder are boosted so as to enhance the self-expressiveness learning and result in better clustering performance. Experiments test FDSC on public datasets and compare with other clustering methods, demonstrating the effectiveness of FDSC.