<p>Unsupervised Domain Adaptation (UDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain with differing data distributions. However, it remains difficult due to noisy pseudo-labels in the target domain, inadequate modeling of local geometric structure, and reliance on a single input view that limits representational diversity in challenging tasks. We propose a framework named Multi-view Affinity-based Projection Alignment (MAPA) that uses a teacher–student network and multi-view augmentation to stabilize pseudo-labels and enhance feature diversity. MAPA transforms each sample into multiple augmented views, constructs a unified affinity matrix that combines semantic cues from pseudo-labels with feature-based distances, and then learns a locality-preserving projection to align source and target data in a shared low-dimensional space. An iterative strategy refines pseudo-labels by discarding low-confidence samples, thereby raising label quality and strengthening supervision for the target domain. MAPA also employs a consistency-weighted fusion mechanism to merge predictions from multiple views, improving stability under domain shift. Finally, MAPA leverages class-centric and cluster-level relationships in the projected space to further refine label assignments, enhancing the overall adaptation process. Experimental results on Office-Home, ImageCLEF, and VisDA-2017 show that MAPA surpasses recent state-of-the-art methods, and it maintains robust performance across backbones including ResNet-50, ResNet-101, and Vision Transformer (ViT).</p>