Abstract: Multi-view subspace clustering (MVSC) optimally integrates multiple graph structure information to improve clustering performance. Recently, many anchor-based variants are proposed to reduce the computational complexity of MVSC. Though achieving considerable acceleration, we observe that most of them adopt fixed anchor points separating from the subsequential anchor graph construction, which may adversely affect the clustering performance. In addition, post-processing is required to generate discrete clustering labels with additional time consumption. To address these issues, we propose a scalable and parameter-free multi-view subspace clustering method to directly output the clustering labels with optimal anchor graph, termed as Efficient One-pass Multi-view Subspace Clustering with Consensus Anchors (EOMSC-CA). Specially, we integrate the anchor points selection and graph construction into a unified optimization framework to boost clustering performance. Meanwhile, by imposing a graph connectivity constraint, our algorithm directly outputs the clustering labels without any post-processing procedures as previous methods do. The proposed approach is proven to has linear time complexity respecting to the data size. Extensive experiments on various benchmark datasets verify the superiority of the proposed method compared to the existing state-of-the-art multi-view subspace clustering competitors over the effectiveness and efficiency. Our code is publicly available at https://github.com/Tracesource/EOMSC-CA.