Multi-view subspace clustering has attracted much attention because of its effectiveness in unsupervised learning. The high time consumption and hyper-parameters are the main obstacles to its development. In this paper, we present a novel method to effectively solve these two defects. First, we employ the bisecting k-means method to generate anchors and construct the hierarchical bipartite graph, which greatly reduce the time consumption. Moreover, we adopt an auto-weighted allocation strategy to learn appropriate weight factors for each view, which can avoid the influence of hyper-parameters. Furthermore, by imposing low rank constraints on the fusion graph, our proposed method can directly obtained the cluster indicators without any post-processing operations. Finally, numerous experiments verify the superiority of proposed method.

中文翻译：

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基于分层二分图的多视图子空间聚类


多视图子空间聚类因其在无监督学习中的有效性而备受关注。高耗时和超参数是其开发的主要障碍。在本文中，我们提出了一种有效解决这两个缺陷的新方法。首先，我们采用一等分 k-means 方法生成锚点并构建分层二分图，大大减少了时间消耗。此外，我们采用自动加权分配策略来为每个视图学习合适的权重因子，这样可以避免超参数的影响。此外，通过对融合图施加低秩约束，我们提出的方法可以直接获得聚类指标，而无需任何后处理操作。最后，大量实验验证了所提方法的优越性。