Deep Multiview Clustering by Contrasting Cluster Assignments
This addresses the challenge of multiview clustering for data analysis, but it appears incremental as it builds on existing deep learning methods with a novel contrastive strategy.
The paper tackles the problem of learning invariant representations in multiview clustering by proposing a cross-view contrastive learning method that contrasts cluster assignments across views, resulting in improved performance over state-of-the-art approaches on several datasets.
Multiview clustering (MVC) aims to reveal the underlying structure of multiview data by categorizing data samples into clusters. Deep learning-based methods exhibit strong feature learning capabilities on large-scale datasets. For most existing deep MVC methods, exploring the invariant representations of multiple views is still an intractable problem. In this paper, we propose a cross-view contrastive learning (CVCL) method that learns view-invariant representations and produces clustering results by contrasting the cluster assignments among multiple views. Specifically, we first employ deep autoencoders to extract view-dependent features in the pretraining stage. Then, a cluster-level CVCL strategy is presented to explore consistent semantic label information among the multiple views in the fine-tuning stage. Thus, the proposed CVCL method is able to produce more discriminative cluster assignments by virtue of this learning strategy. Moreover, we provide a theoretical analysis of soft cluster assignment alignment. Extensive experimental results obtained on several datasets demonstrate that the proposed CVCL method outperforms several state-of-the-art approaches.