LSVC: A Lifelong Learning Approach for Stream-View Clustering

• Published in: IEEE transaction on neural networks and learning systems 2024-08, Vol.PP, p.1-14
• Main Authors: Li, Haoran, Ren, Zhenwen, Guo, Yulan, You, Jiali, You, Xiaojian
• Format: Article
• Language: English
• Subjects: Clustering; Data mining; Feature extraction; Learning systems; Libraries; lifelong and continual learning; multiview learning; Static VAr compensators; stream-view clustering; Streams; Task analysis
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2024.3439394

Multiview clustering (MVC) can achieve more accurate results by utilizing complementary information from multiple perspectives, compared to traditional single-view methods. However, current multiview techniques require all views to be available upfront, making them inadequate for dealing with prevalent data sources that arrive as streams, such as stem cell analysis and multicamera surveillance. To address this problem, in this article, we propose a method called lifelong stream-view clustering (LSVC), which comprises an embedding anchor knowledge library and three key components, enabling the capability to perform asynchronous clustering on stream views. These three components are specifically: 1) the knowledge extraction module that extracts the abstract knowledge of the newcome view over time and updates the shared knowledge library; 2) the knowledge transfer module that aligns the newcome view with the historical knowledge library, enabling the transfer of structure information to the knowledge library; and 3) the knowledge rule module that constraints the knowledge library to enjoy a fair amount of anchors for each cluster, improving the discrimination of knowledge. The experimental results show that LSVC outperforms traditional single-view clustering (SVC) and MVC methods as it gradually improves with the accumulation of stream views and tends to be stable over time.