特征直连与结构化约束的多视图子空间聚类

多视图子空间聚类作为处理多视图数据的聚类算法,其目的在于学习到一个共识的子空间后用于聚类.但是,现存的多视图子空间聚类算法只是将目标放在了原有的多个视图上,忽略了通过特征直连得到的数据.提出的FSMC算法使原有的多个视图与特征直连视图相互学习,通过误差重构和结构化约束子空间得到一个更加合适的子空间表示,同时还考虑了多视...

Feature concatenation and structured constraints based multi-view clustering

Multi-view subspace clustering, as a clustering algorithm for multi-view data, aims to learn a consensus subspace for clustering. However, the existing multi-view clustering algorithms only focus on the original multi-view, ignoring the data obtained by direct feature concatenation. The algorithm proposed in this paper focuses on the mutual learning of the original multi-view and the feature concatenation view, and obtains a more suitable subspace representation through error reconstruction and structural constraint subspace. At the same time, the weight relationship between multi-view and feature concatenation view is also considered. Finally, experiments are conducted on four benchmark datasets to verify the effectiveness of the algorithm.