Contrastive Continual Multi-view Clustering with Filtered Structural Fusion

arXiv:2309.15135v2 Announce Type: replace
Abstract: Multi-view clustering thrives in applications where views are collected in advance by extracting consistent and complementary information among views. However, it overlooks scenarios where data views are collected sequentially, i.e., real-time data. Due to privacy issues or memory burden, previous views are not available with time in these situations. Some methods are proposed to handle it but are trapped in a stability-plasticity dilemma. In specific, these methods undergo a catastrophic forgetting of prior knowledge when …

abstract advance applications arxiv clustering consistent continual cs.ai cs.cv cs.lg data fusion information memory privacy real-time time data type view