DBO-Net: Differentiable bi-level optimization network for multi-view clustering

Abstract

Multi-view clustering on traditional optimization methods is derived from different theoretical frameworks, yet it may be inefficient in dealing with complex multi-view data compared to deep models. In contrast, deep multi-view clustering methods for implicit optimization have excellent feature abstraction ability but are inscrutable due to their black-box problem. However, very limited research was devoted to integrating the advantages of the above two types of methods to design an efficient method for multi-view clustering. Focusing on these problems, this paper proposes a differentiable bi-level optimization network (DBO-Net) for multi-view clustering, which is implemented by incorporating the traditional optimization method with deep learning to design an interpretable deep network. To enhance the representation capability, the proposed DBO-Net is constructed by stacking multiple explicit differentiable block networks to learn an interpretable consistent representation. Then all the learned parameters can be implicitly optimized through back-propagation, making the learned representation more suitable for the clustering task. Extensive experimental results validate that the strategy of bi-level optimization can effectively improve clustering performance and the proposed method is superior to the state-of-the-art clustering methods.