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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Veröffentlicht in:IEEE transaction on neural networks and learning systems Jg. 35; H. 5; S. 6089 - 6102
Hauptverfasser: Zhang, Xiaobo, Wang, Tao, Zhao, Xiaole, Wen, Dengmin, Zhai, Donghai
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.05.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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
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Zusammenfassung: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 initial parameters of these algorithms cause the border samples to fall into a local optimal solution. In this study, a multitask-guided deep clustering (DC) with boundary adaptation (MTDC-BA) based on a convolutional neural network autoencoder (CNN-AE) is proposed. In the first stage, dubbed multitask pretraining (M-train), we construct an autoencoder (AE) named CNN-AE using the DenseNet-like structure, which performs deep feature extraction and stores captured multitask knowledge into model parameters. In the second phase, the parameters of the M-train are shared for CNN-AE, and clustering results are obtained by deep features, which is termed as single-task fitting (S-fit). To eliminate the boundary effect, we use data augmentation and improved self-paced learning to construct the boundary adaptation. We integrate boundary adaptors into the M-train and S-fit stages appropriately. The interpretability of MTDC-BA is accomplished by data transformation. The model relies on the principle that features become important as the reconfiguration loss decreases. Experiments on a series of typical datasets confirm the performance of the proposed MTDC-BA. Compared with other traditional clustering methods, including single-task DC algorithms and the latest multitask clustering algorithms, our MTDC-BA achieves better clustering performance with higher computational efficiency. Deep features clustering results demonstrate the stability of MTDC-BA by visualization and convergence verification. Through the visualization experiment, we explain and analyze the whole model data input and the middle characteristic layer. Further understanding of the principle of MTDC-BA. Through additional experiments, we know that the proposed MTDC-BA is efficient in the use of multitask knowledge. Finally, we carry out sensitivity experiments on the hyper-parameters to verify their optimal performance.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2023.3307126