Structure Mapping Generative Adversarial Network for Multi-View Information Mapping Pattern Mining

Multi-view learning is dedicated to integrating information from different views and improving the generalization performance of models. However, in most current works, learning under different views has significant independency, overlooking common information mapping patterns that exist between the...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 2024-04, Vol.46 (4), p.2252-2266
Main Authors: Bi, Xia-an, Huang, YangJun, Yang, Zicheng, Chen, Ke, Xing, Zhaoxu, Xu, Luyun, Li, Xiang, Liu, Zhengliang, Liu, Tianming
Format: Article
Language:English
Subjects:
Alzheimer's disease
Data mining
Data models
Deep learning
generative adversarial network
Generative adversarial networks
graph convolution
Learning
Mapping
Medical imaging
Multi-view learning
Pattern analysis
Predictive models
structural information mapping
Task analysis
Training
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Summary:Multi-view learning is dedicated to integrating information from different views and improving the generalization performance of models. However, in most current works, learning under different views has significant independency, overlooking common information mapping patterns that exist between these views. This paper proposes a Structure Mapping Generative adversarial network (SM-GAN) framework, which utilizes the consistency and complementarity of multi-view data from the innovative perspective of information mapping. Specifically, based on network-structured multi-view data, a structural information mapping model is proposed to capture hierarchical interaction patterns among views. Subsequently, three different types of graph convolutional operations are designed in SM-GAN based on the model. Compared with regular GAN, we add a structural information mapping module between the encoder and decoder wthin the generator, completing the structural information mapping from the micro-view to the macro-view. This paper conducted sufficient validation experiments using public imaging genetics data in Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. It is shown that SM-GAN outperforms baseline and advanced methods in multi-label classification and evolution prediction tasks.
ISSN:0162-8828
1939-3539
2160-9292
DOI:10.1109/TPAMI.2023.3330795