Not All, but the Right Ones: Energy-Guided Representation Learning for Incomplete Multiview Clustering.

Abstract

Incomplete multiview clustering (IMVC) aims to uncover shared cluster structures from data with partially missing views. Most existing approaches face a critical tradeoff: imputation-free methods struggle under high missingness, while imputation-based methods risk propagating errors from unreliable reconstructions. In contrast, we argue that not all missing views should be recovered-only the right, reliable ones matter. To this end, we propose energy-guided representation learning network (ERL-Net), a novel selective imputation framework that leverages energy-based modeling to adaptively guide feature imputation, fusion, and alignment. ERL-Net integrates four key components: 1) multiview feature extraction using view-specific autoencoders and a shared projection network; 2) energy-geometric graph encoding, which evaluates feature reliability using a learnable energy function and models interview dependencies via joint energy-geometric similarity, enabling instance-specific graph aggregation; 3) energy-gated imputation, which reconstructs missing views using global-to-view mappers, selectively retaining only low-energy (i.e., reliable) candidates; and 4) energy-weighted fusion and alignment, which integrates observed and imputed features into a unified representation, while enforcing semantic consistency across views at both the energy and representation levels. Extensive experiments demonstrate the superiority of ERL-Net, particularly under high missing ratios, achieving significant improvements over state-of-the-art methods across multiple benchmark datasets.