Continual Multiview Spectral Clustering via Multilevel Knowledge

Multiview clustering aims to integrate multiple features from different views to benefit the clustering task, which has attracted much attention in recent years. Most previous research has focused on exploring multiview clustering with a fixed set of tasks. However, it is still challenging to efficiently integrate with new clustering tasks, as it requires repeated access to previous data. To address the above challenges, this letter proposes a novel continual multiview spectral clustering model. The proposed model can efficiently achieve clustering in new tasks by transferring the accumulated knowledge from past tasks, while continuously refining the knowledge to ultimately improve the performance of all clustering tasks. Specifically, the knowledge sharing among different clustering tasks is considered at multiple levels, preserving both the heterogeneous distribution of different views and the relationships between multiple views. Meanwhile, our method is modelled by deep nonlinear structures, which allows to capture more hidden knowledge. In addition, an efficient alternating optimization algorithm is proposed to refine the knowledge online. The superior experimental results on several benchmark datasets show the effectiveness and efficiency of our method compared with other state-of-the-art models.