Multimodal Data Fusion
Published in Advances in Computing, Informatics, Networking and Cybersecurity: A Book Honoring Professor Mohammad S. Obaidat’s Significant Scientific Contributions, 2022
In the era of big data, a large number of multimodal data are constantly generated and accumulated. In the practical application analysis of multimodal big data, the modal incompleteness, real-time processing, modal imbalance and high-dimensional attributes pose severe challenges to the multimodal fusion method. In view of the above characteristics of multimodal data and the shortcomings of existing multimodal fusion algorithms, this chapter studies the multimodal data fusion algorithms from four aspects: incomplete modal analysis fusion, incremental modal clustering fusion, heterogeneous modal migration fusion and low dimensional modal sharing fusion. A series of multimodal data fusion models and algorithms are designed.
Recommended citation: Chen Z, Zhao L, Li Q, et al. Multimodal Data Fusion[M]//Advances in Computing, Informatics, Networking and Cybersecurity: A Book Honoring Professor Mohammad S. Obaidat’s Significant Scientific Contributions. Cham: Springer International Publishing, 2022: 53-91.
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