Multi-View Maximum Entropy Clustering by Jointly Leveraging Inter-View Collaborations and Intra-View-Weighted Attributes

Multi-View Maximum Entropy Clustering by Jointly Leveraging Inter-View Collaborations and Intra-View-Weighted Attributes

As a dedicated countermeasure for heterogeneous multi-view data, multi-view clustering is currently a hot topic in machine learning. However, many existing methods either neglect the effective collaborations among views during clustering or do not distinguish the respective importance of attributes...

Full description

Bibliographic Details
Main Authors: Pengjiang Qian, Jiaxu Zhou, Yizhang Jiang, Fan Liang, Kaifa Zhao, Shitong Wang, Kuan-Hao Su, Raymond F. Muzic
Format: Article
Language:English
Published: IEEE 2018-01-01
Series:IEEE Access
Subjects:
Multi-view clustering
co-clustering
maximum entropy
weighted attribute
Online Access:https://ieeexplore.ieee.org/document/8334539/

Internet

https://ieeexplore.ieee.org/document/8334539/

Similar Items