Scalable Multi-view Clustering based on Tight Anchor Distribution

Multi-view clustering based on anchor graph has gained a lot of attention because of its ability to handle large-scale datasets in linear time. However, the existing methods learn anchors from linear space, ignore the nonlinear manifold characteristics of the original data, and fail to fully consider the geometric topological relationship of anchors in the space, which limits the representability of the anchor graph and makes these algorithms unable to deal with the complex data distribution in real scenarios. In addition, most methods involve the control of multiple hyper-parameters, which often require a significant amount of time for tuning, resulting in algorithms that are not as flexible and scalable as they could be. To address the above problems, this paper proposes Scalable multi-view clustering based on tight anchor distribution (TAD-MVC), where TAD-MVC closely associates neighboring anchors together and learns the degree of closeness between all anchors. In particular, TAD-MVC adaptively evaluates the tightness between anchors and spreads this tight structure with the aim of constraining anchors with a high degree of similarity to the same cluster, while anchors in different clusters will exclude each other as much as possible in order to learn anchors that are sufficiently representative. Experimental results on numerous datasets significantly outperforms the state-of-the-art methods. The code is available at https://github.com/whbdmu/TAD-MVC.