Abstract
Stream View Clustering (SVC) aims to handle view data that is continuously acquired over time. Existing SVC methods align and update the knowledge of newly arrived views with the historical knowledge repository to promote consistency of different view data. However, in streaming scenarios, the arrival sequence of views is unpredictable and continuously evolving. Consequently, early-arriving views tend to dominate the training process, while the subsequently collected views struggle to refine the existing clustering results, resulting in performance bias across different sequences, i.e., Sequential Dependency Problem (SDP). Additionally, the collected data inevitably suffer from the Partial Sample-missing Problem (PSP), which leads to significant biases in the knowledge learned from different views, thereby further exacerbating the SDP. To address these challenges, we propose Adaptive hardness-Driven dictionary distillAtion for incomPlete streaming view clusTering (ADAPT), which is designed to mitigate the adverse effects caused by SDP and PSP. Specifically, we designate the first collected complete view as the teacher view and construct the initial teacher knowledge base through dictionary learning. During subsequent view learning, we employ the teacher prompting imputation strategy to address the PSP problem and ensure the completeness of the collected data. To mitigate the SDP, we propose adaptive hardness-driven dictionary distillation, which can dynamically adjust the distillation strategy according to view quality, thereby enabling robust knowledge learning under different sequences of views. Finally, we introduce cluster guidance learning to further enhance the compactness of the clustering structure, while updating the teacher knowledge base through teacher knowledge summarization. Extensive experiments demonstrate that ADAPT significantly outperforms state-of-the-art methods.