Multi-view clustering is an important and fundamental problem. Many multi-view subspace clustering methods have been proposed, and most of them assume that all views share a same coefficient matrix. However, the underlying information of multi-view data are not fully exploited under this assumption, since the coefficient matrices of different views should have the same clustering properties rather than be uniform among multiple views. To this end, this paper proposes a novel Constrained Bilinear Factorization Multi-view Subspace Clustering (CBF-MSC) method. Specifically, the bilinear factorization with an orthonormality constraint and a low-rank constraint is imposed for all coefficient matrices to make them have the same trace-norm instead of being equivalent, so as to explore the consensus information of multi-view data more fully. Finally, an Augmented Lagrangian Multiplier (ALM) based algorithm is designed to optimize the objective function. Comprehensive experiments tested on nine benchmark datasets validate the effectiveness and competitiveness of the proposed approach compared with several state-of-the-arts.

多视图聚类是一个重要的基本问题。已经提出了许多多视图子空间聚类方法，并且它们中的大多数假设所有视图共享相同的系数矩阵。但是，由于不同视图的系数矩阵应该具有相同的聚类属性，而不是在多个视图之间是统一的，因此在这种假设下不能充分利用多视图数据的基础信息。为此，本文提出了一种新的约束双线性分解多视图子空间聚类（CBF-MSC）方法。具体地，对所有系数矩阵施加具有正交正态约束和低秩约束的双线性分解，以使它们具有相同的迹范而不是相等的迹范，从而更充分地探索多视图数据的共识信息。最后，设计了基于增强拉格朗日乘数（ALM）的算法来优化目标函数。与九个最新技术相比，在九个基准数据集上进行的综合实验验证了该方法的有效性和竞争力。