A Stitch in Time Saves Nine

Incremental multiview clustering (IMVC) leverages consistent information between historical and new views to benefit the clustering task. However, existing IMVC approaches ignore the redundant information in individual views, leading to an accumulation of irrelevance. Besides, with the continuous arrivals of new views, the knowledge learned from historical views is often forgotten, which hinders the learning models from achieving long-term dependencies across incremental views. In this study, we propose a novel progressive information bottleneck (PIB), which is capable of removing redundant information in a timely manner and selectively updating historical knowledge based on information gain of new views. Specifically, to facilitate the knowledge transfer from historical views to incoming one, an information-aware knowledge library is built to store the representative samples of historical views. With the emergence of new views, we first devise a matrix-based mutual information (MI) constraint on an encoder to compress redundant information, which facilitates the training of a neural network with analyzable gradients and obtain a compact yet discriminative representation. Then, a dual-selective updating strategy is proposed to preserve historical knowledge in time when it contributes more to the information gain of the knowledge library than the new view. Finally, relevant samples to the new view in knowledge library are migrated to maximize the cross-level consistency between historical and new views. To the best of our knowledge, this is the first work that designs a gradient-analyzable MI measurement for incremental multiview learning and employs information gain to guide the selective update of the knowledge library. Empirical evaluations on six benchmark datasets show that our method outperforms state-of-the-art baseline methods by an average of 8.1%, 7.6%, and 7.8% on clustering accuracy (ACC), normalized mutual information (NMI), and adjusted Rand index (ARI) metrics, respectively

Bottleneck · Cluster analysis · Discriminative model · Encoder · Information bottleneck method · Knowledge Acquisition · Knowledge base · Advanced Graph Neural Networks · Domain Adaptation and Few-Shot Learning · Recommender Systems and Techniques

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