Representation Learning With Dual Autoencoder for Multi-Label Classification

Representation Learning With Dual Autoencoder for Multi-Label Classification

Multi-label classification aims to deal with the problem that an object may be associated with one or more labels, which is a more difficult task due to the complex nature of multi-label data. The crucial problem of multi-label classification is the more robust and higher-level feature representatio...

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Bibliographic Details
Main Authors: Yi Zhu, Yang Yang, Yun Li, Jipeng Qiang, Yunhao Yuan, Runmei Zhang
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Multi-label classification
dual autoencoder
RICA
manifold regularization
representation learning
Online Access:https://ieeexplore.ieee.org/document/9481139/

Internet

https://ieeexplore.ieee.org/document/9481139/

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