Multi-view feature embedding via shared and specific structural contrastive learning

Abstract

Multi-view feature embedding (MvFE) is a powerful technique for addressing the challenges posed by high-dimensional multi-view data. In recent years, contrastive learning (CL) has gained significant attention due to its superior performance. However, existing CL-based methods primarily focus on promoting consistency between any two cross views, thereby overlooking the diversity among views and impeding the simultaneous exploration of both consistency and complementarity. In this study, we propose a novel MvFE method called shared and specific structural contrastive learning (S3CL), which constructs shared and specific losses to capture both shared and specific potential structural information in multi-view data. Additionally, S3CL introduces a novel view-weighting mechanism that adaptively assigns weights to each specific losses, enabling a discriminative treatment of each view based on its uniqueness and importance in the feature embedding process. Moreover, to fully explore the view-specific structures while avoiding the emergence of pseudo-structures, a residual mechanism of incomplete fitting is employed in S3CL. Experimental results on five real-world datasets validate the superior performance of our proposed method compared to existing approaches.