计算机科学
聚类分析
矩阵分解
可解释性
利用
杠杆(统计)
理论计算机科学
非负矩阵分解
张量(固有定义)
算法
人工智能
数学
特征向量
物理
量子力学
纯数学
计算机安全
作者
Jing Li,Qianqian Wang,Ming Yang,Quanxue Gao,Xinbo Gao
标识
DOI:10.1109/tmm.2023.3340095
摘要
Due to the excellent interpretability of non-negative matrix factorization (NMF), NMF-based multi-view clustering has attracted much attention for multi-media data analysis and processing. However, the existing clustering methods leverage NMF to cluster data matrix, resulting in high computational complexity. Moreover, they are sub-optimal to exploit the complementary information between views because they all measure the between-views error pixel by pixel. To tackle this problem, inspired by orthogonal NMF and anchor graph, we present an efficient anchor graph factorization model with orthogonal, nonnegative, and tensor low-rank constraints. We use an anchor graph instead of a data matrix to get an indicator matrix without post-processing, which remarkably reduces the computational complexity. To exploit the between-views complementary information well, we introduce tensor Schatten p -norm regularization on the third tensor, composed of soft label matrices of views. The solution can be obtained by iteratively optimizing four decoupled sub-problems, which can be solved more efficiently with good convergence. Through experimental results on the six multi-view datasets, our approach ensures the enhancement of clustering performance while improving efficiency.
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