Kang Zhao, Lin Zhiping, Zhu Xiaofeng, Xu Wenbo
IEEE Trans Cybern. 2022 Sep;52(9):8976-8986. doi: 10.1109/TCYB.2021.3061660. Epub 2022 Aug 18.
Graph-based subspace clustering methods have exhibited promising performance. However, they still suffer some of these drawbacks: they encounter the expensive time overhead, they fail to explore 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 an n×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 multiview data is also proposed, which is linearly 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.
基于图的子空间聚类方法已展现出良好的性能。然而，它们仍存在一些缺点：它们面临高昂的时间开销，无法探索显式聚类，并且不能推广到未见过的数据点。在这项工作中，我们提出了一个可扩展的图学习框架，旨在同时解决上述三个挑战。具体而言，它基于锚点和二分图的思想。我们不是构建一个n×n的图（其中n是样本数量），而是构造一个二分图来描述样本与锚点之间的关系。同时，采用连通性约束以确保连通分量直接指示聚类。我们进一步建立了我们的方法与K均值聚类之间的联系。此外，还提出了一个处理多视图数据的模型，该模型相对于n是线性可扩展的。大量实验证明了我们的方法相对于许多现有聚类方法的效率和有效性。
