Multimodal deep learning for solar radio burst classification
文献类型：期刊论文
| 作者 | Ma, Lin1,2; Chen, Zhuo2 ; Xu, Long2; Yan, Yihua2
|
| 刊名 | PATTERN RECOGNITION
 |
| 出版日期 | 2017 |
| 卷号 | 61页码:573-582 |
| 关键词 | Multimodal learning Solar radio spectrum Classification |
| 英文摘要 | In this paper, multimodal deep learning for solar radio burst classification is proposed. We make the first attempt to build multimodal learning network to learn the joint representation of the solar radio spectrums captured from different frequency channels, which are treated as different modalities. In order to learn the representation of each modality and the correlation and interaction between different modalities, autoencoder together with the structured regularization is used to enforce and learn the modality-specific sparsity and density of each modality, respectively. Fully connected layers are further employed to exploit the relationships between different modalities for the joint representation generation of the solar radio spectrums. Based on the learned joint representation, solar radio burst classification is performed. With the validation on the constructed solar radio spectrum database, experimental results have demonstrated that the proposed multimodal learning network can effectively learn the representation of the solar radio spectrum, and improve the classification accuracy. (C) 2016 Elsevier Ltd. All rights reserved. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
| 研究领域[WOS] | Computer Science ; Engineering |
| 关键词[WOS] | DIMENSIONALITY ; REGRESSION |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000385899400044 |
| 源URL | [http://ir.bao.ac.cn/handle/114a11/6900]  |
| 专题 | 国家天文台_太阳物理研究部 |
| 作者单位 | 1.Huawei Noahs Ark Lab, Hong Kong, Hong Kong, Peoples R China 2.Chinese Acad Sci, Key Lab Solar Act, Natl Astron Observ, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ma, Lin,Chen, Zhuo,Xu, Long,et al. Multimodal deep learning for solar radio burst classification[J]. PATTERN RECOGNITION,2017,61:573-582. |
| APA | Ma, Lin,Chen, Zhuo,Xu, Long,&Yan, Yihua.(2017).Multimodal deep learning for solar radio burst classification.PATTERN RECOGNITION,61,573-582. |
| MLA | Ma, Lin,et al."Multimodal deep learning for solar radio burst classification".PATTERN RECOGNITION 61(2017):573-582. |
入库方式： OAI收割
来源：国家天文台
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