A cross-disciplinary comparison of multimodal data fusion approaches and applications: Accelerating learning through trans-disciplinary information sharing

作者：

Highlights：

• Multimodal data fusion approaches are used across widely disparate disciplines.

• Potentially fruitful cross-disciplinary insights are identified via a meta-analysis.

• Fusion algorithm development is needed for high dimensionality and time-series data.

• Shared vocabulary for multimodal data fusion could spur successes across disciplines.

摘要

•Multimodal data fusion approaches are used across widely disparate disciplines.•Potentially fruitful cross-disciplinary insights are identified via a meta-analysis.•Fusion algorithm development is needed for high dimensionality and time-series data.•Shared vocabulary for multimodal data fusion could spur successes across disciplines.

论文关键词：Multimodal data fusion,Machine learning,Complex systems,Big data,Trans-disciplinary

论文评审过程：Received 16 December 2019, Revised 3 July 2020, Accepted 13 August 2020, Available online 27 August 2020, Version of Record 8 September 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.113885