Transaction

jmetaB03acefea01b682355ddd1856dadd9017121d2d4f08faeabe905fc663649f2faf96@f9851c2160f6d07cecd53064b44b75ae180251462771b97e11dbfad2c8f6cbe5rss.itemmetarss.netM…<item> <title>Federated Incomplete Multi-View Clustering with Heterogeneous Graph Neural Networks</title> <link>https://arxiv.org/abs/2406.08524</link> <description>arXiv:2406.08524v1 Announce Type: cross Abstract: Federated multi-view clustering offers the potential to develop a global clustering model using data distributed across multiple devices. However, current methods face challenges due to the absence of label information and the paramount importance of data privacy. A significant issue is the feature heterogeneity across multi-view data, which complicates the effective mining of complementary clustering information. Additionally, the inherent incompleteness of multi-view data in a distributed setting can further complicate the clustering process. To address these challenges, we introduce a federated incomplete multi-view clustering framework with heterogeneous graph neural networks (FIM-GNNs). In the proposed FIM-GNNs, autoencoders built on heterogeneous graph neural network models are employed for feature extraction of multi-view data at each client site. At the server level, heterogeneous features from overlapping samples of each client are aggregated into a global feature representation. Global pseudo-labels are generated at the server to enhance the handling of incomplete view data, where these labels serve as a guide for integrating and refining the clustering process across different data views. Comprehensive experiments have been conducted on public benchmark datasets to verify the performance of the proposed FIM-GNNs in comparison with state-of-the-art algorithms.</description> <guid isPermaLink="false">oai:arXiv.org:2406.08524v1</guid> <category>cs.LG</category> <category>cs.DC</category> <arxiv:announce_type>cross</arxiv:announce_type> <dc:rights>http://creativecommons.org/licenses/by-nc-nd/4.0/</dc:rights> <dc:creator>Xueming Yan, Ziqi Wang, Yaochu Jin</dc:creator> </item>