V2V : A Deep Learning Approach to Variable-to-Variable Selection and Translation for Multivariate Time-Varying Data
We present V2V, a novel deep learning framework, as a general-purpose solution to the variable-to-variable (V2V) selection and translation problem for multivariate time-varying data (MTVD) analysis and visualization. V2V leverages a representation learning algorithm to identify transferable variable...
Ausführliche Beschreibung
Bibliographische Detailangaben
| Veröffentlicht in: | IEEE transactions on visualization and computer graphics. - 1998. - 27(2021), 2 vom: 28. Feb., Seite 1290-1300
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| 1. Verfasser: |
Han, Jun
(VerfasserIn) |
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| Weitere Verfasser: |
Zheng, Hao,
Xing, Yunhao,
Chen, Danny Z,
Wang, Chaoli |
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| Format: | Online-Aufsatz
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| Sprache: | English |
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| Veröffentlicht: |
2021
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on visualization and computer graphics
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| Schlagworte: | Journal Article |
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