Decomposed deep multi-view subspace clustering with self-labeling supervision

Abstract

Most deep multi-view subspace clustering (DMVSC) methods usually employ pipeline optimization by learning the self-expression with a deep model first and then applying spectral clustering to group multi-view data. Subsequently, end-to-end DMVSC methods have been proposed by integrating these two steps into a unified optimization framework. However, the pipeline methods may suffer from misaligned clustering accumulation due to the noise or outlying entries, and the end-to-end methods often constitute a relatively complex parameter optimization. In this paper, we propose a novel method named decomposed deep multi-view subspace clustering with self-labeling supervision (D2MVSC) that runs with a decomposed optimization strategy by three-stage training. Specifically, multi-scale features are extracted by autoencoder in the pre-training stage. According to the discriminative contribution of each view, consensus self-expression is learned from these features by adaptive fusion and structure supervision to generate high-quality pseudo-labels in the fine-tuning stage. Finally, the pseudo-labels are used to retrain the model in a self-labeling supervision manner for robust clustering. Exciting, the self-labeling supervision can be used as an add-on module for other DMVSC methods to improve clustering performance. Extensive experiments on six datasets verify the effectiveness and superiority of our method over other state-of-the-art methods.