Graph-based subspace clustering methods have exhibited promising performance. However, they still suffer some of these drawbacks: encounter the expensive time overhead, fail in exploring the explicit clusters, and cannot generalize to unseen data points. In this work, we propose a scalable graph learning framework, seeking to address the above three challenges simultaneously. Specifically, it is based on the ideas of anchor points and bipartite graph. Rather than building a $n\times n$ graph, where $n$ is the number of samples, we construct a bipartite graph to depict the relationship between samples and anchor points. Meanwhile, a connectivity constraint is employed to ensure that the connected components indicate clusters directly. We further establish the connection between our method and the K-means clustering. Moreover, a model to process multi-view data is also proposed, which is linear scaled with respect to $n$. Extensive experiments demonstrate the efficiency and effectiveness of our approach with respect to many state-of-the-art clustering methods.

翻译：以图形为基础的子空间群集方法表现良好。 但是,它们仍然有一些缺点:遇到昂贵的时间管理,无法探索明确的群集,无法将数据点概括为无形的数据点。 在这项工作中,我们提出了一个可缩放的图表学习框架,试图同时解决上述三个挑战。具体地说,它基于锚点和双面图的概念。我们不是建立一个以美元计价的“美元”图,而是建立一个显示样品和锚点之间关系的双边图。与此同时,我们使用连接性限制来确保连接的组件直接表示群集。我们进一步确定我们的方法与K- means群集之间的联系。此外,还提出了处理多视图数据的模型,该模型以美元为线性标价标价。广泛的实验表明我们在许多最先进的群集方法方面的做法的效率和效力。