UNTIE: Clustering analysis with disentanglement in multi-view information fusion

Abstract

Multi-view clustering focuses on exploring cluster structures among multiple views and is an effective approach to achieve multi-view information fusion without requiring label supervision. However, multiple views’ useful and meaningless information usually is entangled and the latter might cause negative influences in the fusion process. In this paper, we research the interpretability for different information in multiple views and propose a novel method (termed UNTIE) to address their entanglement. Specifically, in UNTIE, discrete view-common variable is introduced to explore all views’ common information, and continuous view-private variables are introduced to learn individual views’ private information. In this way, the learned representations are disentangled and interpretable where each variable represents a single factor among multi-view data. Then, the useful information in disentangled view-common and view-private representations are leveraged to conduct comprehensive multi-view clustering, making UNTIE can explore common and complementary information from multiple views while obtaining the robustness to meaningless information. Finally, UNTIE has the ability to controllably generate samples and extensive experiments demonstrate its superior representation ability and clustering performance.