Peng Chong, Zhang Jing, Chen Yongyong, Xing Xin, Chen Chenglizhao, Kang Zhao, Guo Li, Cheng Qiang
College of Computer Science and Technology, Qingdao University, China.
School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), China.
Knowl Based Syst. 2022 Dec 22;258. doi: 10.1016/j.knosys.2022.109915. Epub 2022 Sep 24.
Subspace clustering algorithms have been found successful in various applications that involve two-dimensional data, i.e., each example of the data is a matrix. However, most of the existing methods transform the matrix-type examples to vectors in a pre-processing step, which omits and severely damages the inherent structural information of such data. In this paper, we propose a novel subspace clustering method for two-dimensional data, which is capable of extracting the most representative structural information from the data to recover the underlying grouping relationships of the data. The structural features are extracted from two views of the data and the numbers of feature spaces in both views are automatically determined by optimization. Extensive experiments confirm the effectiveness of the proposed method.
子空间聚类算法已在各种涉及二维数据的应用中取得成功，即数据的每个示例都是一个矩阵。然而，大多数现有方法在预处理步骤中将矩阵类型的示例转换为向量，这忽略并严重破坏了此类数据的固有结构信息。在本文中，我们提出了一种针对二维数据的新型子空间聚类方法，该方法能够从数据中提取最具代表性的结构信息，以恢复数据的潜在分组关系。结构特征从数据的两个视图中提取，并且两个视图中的特征空间数量通过优化自动确定。大量实验证实了所提方法的有效性。
