DAS-PMVC: A Framework for Partial Multi-View Clustering via Dual Alignment and Structure Enhancement

2026-07-30

AI Summary

This work addresses the partially view-aligned problem (PVAP) in multi-view clustering caused by missing data by proposing a dual-alignment framework that integrates anchor graph alignment with contrastive learning. The method constructs anchor graphs to achieve cross-view structural alignment and leverages graph convolutional networks combined with structure-enhanced deep feature learning to improve the discriminability of incomplete multi-view representations within a unified latent space. To further refine sample correspondence, it employs the Hungarian algorithm for optimal matching and adopts a synergistic strategy of pre-training and joint embedding for collaborative optimization. Extensive experiments demonstrate that the proposed approach significantly outperforms state-of-the-art methods across multiple benchmark datasets, achieving substantial improvements in both clustering accuracy and robustness.

Abstract

In recent years, multi-view clustering has attracted widespread research interest. However, due to limitations in data collection devices, data across different views often suffer from misalignment, leading to the partial view alignment problem (PVAP). To mitigate the impact of view asymmetry and irrelevant samples, this paper proposes a framework for partial multi-view clustering via dual alignment and structure enhancement (DAS-PMVC), which leverages view structure consistency and semantic relevance. Specifically, DAS-PMVC includes three parts: \textbf{anchor graph structure alignment}, where sample joint embedding representations with consistent latent space are derived from anchor point relationships for initial view alignment; \textbf{structure-enhanced feature learning}, where the model learns view structure information through pretraining and combines multi-view graph convolutional networks to further extract deep latent features from the aligned graph structure to improve the discriminative power of representations; and \textbf{a dual alignment strategy}, where initial alignment is performed through the anchor graph in the pretraining phase, and contrastive learning loss and the Hungarian algorithm are introduced in the training phase to further optimize the alignment of latent features. Experimental results on various datasets demonstrate that the DAS-PMVC framework outperforms existing state-of-the-art methods in clustering performance, showcasing its effectiveness and superiority.

Problem

• partial multi-view clustering
• view misalignment
• view asymmetry
• incomplete multi-view data
• multi-view alignment

Innovation

• partial multi-view clustering
• dual alignment
• structure enhancement
• anchor graph
• contrastive learning