Distribution-Level Multi-View Clustering for Unaligned Data

Recently, many multi-view clustering (MVC) methods have achieved promising results through integrating complementary and consensus information from different views in the fields of signal processing and machine learning. However, most of the methods require complete or partial correspondence of multi-view instances which is hard to satisfy in many practical applications. To this end, this letter proposes a novel method termed Distribution-Level Multi-view Clustering for Unaligned Data (DLCU), which proves to be well-suited for scenarios where instance correspondences between different modalities are entirely absent. Specifically, in order to reconstruct cross-view correspondence, the wasserstein distance is employed to effectuate the alignment of multi-view data in distribution-level and guide the learning of cross-view transformation. Furthermore, the negative impact of the inevitable misalignment is mitigated through the global attention mechanism, which is designed to assign appropriate weights to realigned instances across multiple views. Besides, a divergence-based clustering objective is explored to encourage a clear cluster structure and conduct the training of latent representation. Experimental results on several real-world datasets show our promising performance comparing with the state-of-the-art methods.