Multiview spectral clustering via complementary information
Shuangxun Ma
School of Software Enfineering, Xi'an Jiaotong University, Shannxi, China
Search for more papers by this authorCorresponding Author
Yuehu Liu
Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Shannxi, China
Correspondence Yuehu Liu, Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Shannxi, China.
Email: [email protected]
Search for more papers by this authorQinghai Zheng
School of Software Enfineering, Xi'an Jiaotong University, Shannxi, China
Search for more papers by this authorYaochen Li
School of Software Enfineering, Xi'an Jiaotong University, Shannxi, China
Search for more papers by this authorZhichao Cui
Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Shannxi, China
Search for more papers by this authorShuangxun Ma
School of Software Enfineering, Xi'an Jiaotong University, Shannxi, China
Search for more papers by this authorCorresponding Author
Yuehu Liu
Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Shannxi, China
Correspondence Yuehu Liu, Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Shannxi, China.
Email: [email protected]
Search for more papers by this authorQinghai Zheng
School of Software Enfineering, Xi'an Jiaotong University, Shannxi, China
Search for more papers by this authorYaochen Li
School of Software Enfineering, Xi'an Jiaotong University, Shannxi, China
Search for more papers by this authorZhichao Cui
Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Shannxi, China
Search for more papers by this authorFunding information: National Natural Science Foundation of China, 61973245
Abstract
In this article, multiview spectral clustering via complementary information (MSCC) is proposed, in which both the consensus information and the complementary information are explored for multiview clustering. In contrast to most multiview spectral clustering methods, the proposed MSCC considers the differences among multiple views and constructs a similarity matrix for clustering. Furthermore, a convex relaxation is employed and an algorithm that is based on the augmented Lagrange multiplier is proposed for optimizing the objective function of MSCC. In extensive experiments on five real-world benchmark datasets, our proposed method outperforms two baselines and has significantly improved to several state-of-the-art multiview clustering methods.
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