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|a 10.1007/s10462-022-10332-z
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|a eng
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| 100 |
1 |
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|a Xie, Zhuyang
|e verfasserin
|4 aut
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| 245 |
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|a Deep learning on multi-view sequential data
|b a survey
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|c 2023
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|a Text
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|a ƒaComputermedien
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|a Date Revised 18.09.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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|a With the progress of human daily interaction activities and the development of industrial society, a large amount of media data and sensor data become accessible. Humans collect these multi-source data in chronological order, called multi-view sequential data (MvSD). MvSD has numerous potential application domains, including intelligent transportation, climate science, health care, public safety and multimedia, etc. However, as the volume and scale of MvSD increases, the traditional machine learning methods become difficult to withstand such large-scale data, and it is no longer appropriate to use hand-craft features to represent these complex data. In addition, there is no general framework in the process of mining multi-view relationships and integrating multi-view information. In this paper, We first introduce four common data types that constitute MvSD, including point data, sequence data, graph data, and raster data. Then, we summarize the technical challenges of MvSD. Subsequently, we review the recent progress in deep learning technology applied to MvSD. Meanwhile, we discuss how the network represents and learns features of MvSD. Finally, we summarize the applications of MvSD in different domains and give potential research directions
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|a Journal Article
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|a Deep neural networks
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|a Multi-view
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|a Sequential data
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| 650 |
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|a Spatio-temporal
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| 700 |
1 |
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|a Yang, Yan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Yiling
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Jie
|e verfasserin
|4 aut
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| 700 |
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|a Du, Shengdong
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t Artificial intelligence review
|d 1998
|g 56(2023), 7 vom: 09., Seite 6661-6704
|w (DE-627)NLM098184490
|x 0269-2821
|7 nnns
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| 773 |
1 |
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|g volume:56
|g year:2023
|g number:7
|g day:09
|g pages:6661-6704
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|u http://dx.doi.org/10.1007/s10462-022-10332-z
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