Multitask-Guided Deep Clustering With Boundary Adaptation

Multitask learning uses external knowledge to improve internal clustering and single-task learning. Existing multitask learning algorithms mostly use shallow-level correlation to aid judgment, and the boundary factors on high-dimensional datasets often lead algorithms to poor performance. The initia...

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Bibliographic Details
Published in:	IEEE transaction on neural networks and learning systems Vol. 35; no. 5; pp. 6089 - 6102
Main Authors:	Zhang, Xiaobo, Wang, Tao, Zhao, Xiaole, Wen, Dengmin, Zhai, Donghai
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.05.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Adaptation Algorithms Artificial neural networks Boundary adaptation Clustering Clustering algorithms Convolutional neural networks Correlation Data augmentation Data mining Data models Datasets deep learning explainable method Feature extraction Learning Machine learning Mathematical models multitask learning Neural networks Parameter sensitivity Parameters Principles Reconfiguration Task analysis Visualization
ISSN:	2162-237X, 2162-2388, 2162-2388
Online Access:	Get full text
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