Multi-view anchor graph clustering selects representative anchors to avoid full pair-wise similarities and therefore reduce the complexity of graph methods. Although widely applied in large-scale applications, existing approaches do not pay sufficient attention to establishing correct correspondences between the anchor sets across views. To be specific, anchor graphs obtained from different views are not aligned column-wisely. Such an \textbf{A}nchor-\textbf{U}naligned \textbf{P}roblem (AUP) would cause inaccurate graph fusion and degrade the clustering performance. Under multi-view scenarios, generating correct correspondences could be extremely difficult since anchors are not consistent in feature dimensions. To solve this challenging issue, we propose the first study of the generalized and flexible anchor graph fusion framework termed \textbf{F}ast \textbf{M}ulti-\textbf{V}iew \textbf{A}nchor-\textbf{C}orrespondence \textbf{C}lustering (FMVACC). Specifically, we show how to find anchor correspondence with both feature and structure information, after which anchor graph fusion is performed column-wisely. Moreover, we theoretically show the connection between FMVACC and existing multi-view late fusion \cite{liu2018late} and partial view-aligned clustering \cite{huang2020partially}, which further demonstrates our generality. Extensive experiments on seven benchmark datasets demonstrate the effectiveness and efficiency of our proposed method. Moreover, the proposed alignment module also shows significant performance improvement applying to existing multi-view anchor graph competitors indicating the importance of anchor alignment. Our code is available at \url{https://github.com/wangsiwei2010/NeurIPS22-FMVACC}.

翻译：多观点锚图群集选择有代表性的锚,以避免完全对称相似性,从而降低图形方法的复杂性。 虽然在大规模应用中广泛应用了正确对应性 。 虽然现有方法没有足够重视在各种视图的锁定组间建立正确对应性。 具体地说, 从不同视图中获取的锁定图不是对齐的列。 这样的 \ textbf{ A} hurchor-\ textbf{ U}+U} 校正 将导致不准确的图形粘合并降低组合性。 在多观点假设下, 生成正确对应性可能非常困难, 因为锁定在功能层面不一致 。 为了解决这个具有挑战性的问题, 我们提议对通用和灵活的锚定点图集组合组合框架进行第一项研究, 称之为\ textbf{A} nchorchor- textbf{B}A} nchnocklebleoral- textbf{C} orblick} orbld_ prespresent comblight view view view view view (FMilding) Syal comliews) Syal real press press press press real pressional commailding pressional pressional pressionality relight.