Multi-Label Feature Selection Based on High-Order Label Correlation Assumption

Multi-Label Feature Selection Based on High-Order Label Correlation Assumption

Multi-label data often involve features with high dimensionality and complicated label correlations, resulting in a great challenge for multi-label learning. Feature selection plays an important role in multi-label learning to address multi-label data. Exploring label correlations is crucial for mul...

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Bibliographic Details
Main Authors: Ping Zhang, Wanfu Gao, Juncheng Hu, Yonghao Li
Format: Article
Language:English
Published: MDPI AG 2020-07-01
Series:Entropy
Subjects:
multi-label learning
multi-label feature selection
information theory
Max-Correlation
Online Access:https://www.mdpi.com/1099-4300/22/7/797

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