Large-scale Multi-View Clustering (LMVC) is a hot research problem in the fields of signal processing and machine learning, and many anchor-based multi-view subspace clustering algorithms are proposed in recent years. However, most existing methods usually concentrate on the issue of reducing the time cost and ignore the exploration of the complementary information during the clustering process. To this end, we propose a Fast Essential Subspace Representation Learning (FESRL) method for large-scale multi-view subspace clustering. Specifically, FESRL introduces the orthogonal transformation to investigate both the complementary and consensus information across multiple views. The essential subspace representation can be learned in a linear time cost. Experiments conducted on several benchmark datasets illustrate the competitiveness of the proposed method.

中文翻译：

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通过快速基本子空间表示学习进行大规模多视图聚类

大规模多视图聚类（LMVC）是信号处理和机器学习领域的一个热点研究问题，近年来提出了许多基于anchor的多视图子空间聚类算法。然而，大多数现有方法通常集中在降低时间成本的问题上，而忽略了在聚类过程中对互补信息的探索。为此，我们提出了一种用于大规模多视图子空间聚类的快速基本子空间表示学习（FESRL）方法。具体来说，FESRL 引入了正交变换来研究跨多个视图的互补和共识信息。基本子空间表示可以在线性时间成本中学习。