Contrastive Continual Multi-view Clustering with Filtered Structural Fusion
arXiv (Cornell University)(2023)
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 a new view is
attained. Such a catastrophic forgetting problem (CFP) would cause the
consistent and complementary information hard to get and affect the clustering
performance. To tackle this, we propose a novel method termed Contrastive
Continual Multi-view Clustering with Filtered Structural Fusion (CCMVC-FSF).
Precisely, considering that data correlations play a vital role in clustering
and prior knowledge ought to guide the clustering process of a new view, we
develop a data buffer with fixed size to store filtered structural information
and utilize it to guide the generation of a robust partition matrix via
contrastive learning. Furthermore, we theoretically connect CCMVC-FSF with
semi-supervised learning and knowledge distillation. Extensive experiments
exhibit the excellence of the proposed method.
MoreTranslated text
Key words
filtered structural
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