Soft-Label Tensor Learning for Continual Multi-View Clustering

Multi-view clustering (MVC) partitions data with multiple views in an unsupervised manner. Commonly, it assumes that all data views are available in advance; however, in many real-world scenarios, new data views are accumulated over time, bringing the topic of continual multi-view clustering (CMVC). In this paper, we suggest two key constraints that the task of CMVC should follow: cross-session information utilization, given the limitations on historical data, newly arrived view information and retained historical clustering information should be effectively utilized for cross-session knowledge transfer; structural stability, in an incremental scenario, the clustering structure must be updated in a stable manner. Specifically, to achieve these constraints, we introduce the probability membership module to learn the relationship between current soft labels and historical soft labels, thereby identifying useful information introduced by the current view while leveraging historical clustering information. We further introduce the tensor correction learning module, which jointly models the current soft labels, historical soft labels, and the learned consistency structure as a third-order tensor. It employs the Low-Bias Refined norm to suppress redundant structural components while retaining the dominant ones, thereby enhancing the stability of the clustering structure. Upon these above ideas, we propose a novel CMVC method, namely Soft-Label Tensor Learning for Continual Multi-View Clustering (STL-CMVC). We conduct extensive experiments to evaluate STL-CMVC on multiple benchmark datasets. Empirical results demonstrate that STL-CMVC can significantly outperform existing baseline methods.