Progressive Multi-View Graph Projection for Robust Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) remains challenged by an unstable target structure, pseudo-label noise, and heterogeneous transfer difficulty across domains. To address these issues, we propose Progressive Multi-View Graph Projection (PMGP), a two-stage framework that first learns transferable representations via source supervision, domain-adversarial training, and teacher–student consistency and then performs latent-space refinement through multi-view graph construction and projection learning. Specifically, three perturbation-induced views are considered for each sample: the original view, an input-space patch-masked view, and a representation-space feature-dimension masked view. After joint preprocessing with PCA and L2 normalization, PMGP constructs per-view affinity graphs by combining geometric neighborhood relations with pseudo-supervised semantic relations, and applies locality-preserving projection to learn a structure-aware shared subspace. In this subspace, target pseudo-labels are iteratively refined using source prototypes, target class centers, and progressive confidence filtering. Experiments on Office-Home, ImageCLEF-DA, and VisDA-2017 show that PMGP achieves competitive performance and stable behavior across different benchmark settings and backbone architectures. These results indicate that multi-view graph refinement provides an effective and interpretable way to improve target structure estimation and reduce pseudo-label error accumulation in UDA.