Multi-view subspace clustering for learning joint representation via low-rank sparse representation

被引:9
|
作者
Khan, Ghufran Ahmad [1 ,2 ]
Hu, Jie [1 ,3 ,4 ,5 ]
Li, Tianrui [1 ,3 ,4 ,5 ]
Diallo, Bassoma [1 ]
Du, Shengdong [1 ,3 ,4 ,5 ]
机构
[1] Southwest Jiaotong Univ, Sch Comp & Artificial Intelligence, Chengdu 611756, Peoples R China
[2] Koneru Lakshmaiah Educ Fdn, Dept Comp Sci & Engn, Vaddeswaram 522502, Andhra Pradesh, India
[3] Minist Educ, Engn Res Ctr Sustainable Urban Intelligent Transpo, Chengdu 611756, Peoples R China
[4] Southwest Jiaotong Univ, Natl Engn Lab Integrated Transportat Big Data Appl, Chengdu 611756, Peoples R China
[5] Southwest Jiaotong Univ, Mfg Ind Chains Collaborat & Informat Support Techn, Chengdu 611756, Peoples R China
基金
中国国家自然科学基金;
关键词
Subspace clustering; Low-rank representation; Self-representation structure; Nearest neighbor; ALGORITHM; MATRIX;
D O I
10.1007/s10489-023-04716-z
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Multi-view data are generally collected from distinct sources or domains characterized by consistent and specific properties. However, most existing multi-view subspace clustering approaches solely encode the self-representation structure through consistent representation or a set of specific representations, leaving the knowledge of the individual view unexploited and resulting in bad performance in self-representation structure. To address this issue, we propose a novel subspace clustering strategy in which the self-representation structure is contemplated through consistent and specific representations. Specifically, we apply the low-rank sparse representation scenario to uncover the global shared representation structure among all the views and deploy the nearest neighboring method to preserve the geometrical structure according to the consistent and specific representation. The L-1-norm and frobenius norm are applied to the consistent and specific representation to promote a sparser solution and guarantee a grouping effect. Besides, a novel objective function is figured out, which goes under the optimization process through the alternating direction technique to evaluate the optimal solution. Finally, experiments conducted on several benchmark datasets show the effectiveness of the proposed method over several state-of-the-art algorithms.
引用
收藏
页码:22511 / 22530
页数:20
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