OPTION: Optimal Transport–Guided Flow Matching for Incomplete and Unaligned Multi-View Clustering

Abstract

Multi-view clustering effectively exploits rich information from multiple views, yet real-world applications are frequently challenged by missing views and cross-view sample misalignment, hindering cross-view modeling and resulting inferior clustering performance. To address these challenges, this paper presents a novel method, **OP**timal **T**ransport–gu**I**ded fl**O**w matchi**N**g for incomplete and unaligned multi-view clustering (**OPTION**). Specifically, OPTION employs conditional flow matching to learn deterministic transport paths for missing-view imputation, enabling stable manifold-preserving recovery and more discriminative representations. To achieve alignment-free fusion, we introduce a Gromov-Wasserstein loss—a structural relaxation of optimal transport—that aligns intra-view geometric structures in the latent space. Furthermore, an optional contrastive regularization is incorporated to enhance cross-view consistency specifically for aligned settings. Extensive experiments demonstrate that OPTION outperforms state-of-the-art methods across ideal, incomplete, and unaligned scenarios.