A Comparative Analysis of Three Data Fusion Methods and Construction of the Fusion Method Selection Paradigm

Ziqi Liu, Ziqiao Yin (), Zhilong Mi, Binghui Guo () and Zhiming Zheng
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Ziqi Liu: School of Mathematical Sciences, Beihang University, Beijing 100191, China
Ziqiao Yin: Key Laboratory of Mathematics, Informatics and Behavioral Semantics and State, Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, China
Zhilong Mi: Key Laboratory of Mathematics, Informatics and Behavioral Semantics and State, Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, China
Binghui Guo: Key Laboratory of Mathematics, Informatics and Behavioral Semantics and State, Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, China
Zhiming Zheng: Key Laboratory of Mathematics, Informatics and Behavioral Semantics and State, Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, China

Mathematics, 2025, vol. 13, issue 8, 1-21

Abstract: Multisource and multimodal data fusion plays a pivotal role in large-scale artificial intelligence applications involving big data. However, the choice of fusion strategies for different scenarios is often based on experimental comparisons, which leads to increased computational costs during model training and suboptimal performance during testing. In this paper, we present a theoretical analysis of early fusion, late fusion, and gradual fusion methods. We derive equivalence conditions between early and late fusions within the framework of generalized linear models. Moreover, we analyze the failure conditions of early fusion in the presence of nonlinear feature-label relationships. Furthermore, we propose an approximate equation for evaluating the accuracy of early and late fusion methods as a function of sample size, feature quantity, and modality number. We also propose a critical sample size threshold at which the performance dominance of early fusion and late fusion models undergoes a reversal. Finally, we introduce a fusion method selection paradigm for selecting the most appropriate fusion method prior to task execution and demonstrate its effectiveness through extensive numerical experiments. Our theoretical framework is expected to solve the problems of computational and resource costs in model construction, improving the scalability and efficiency of data fusion methods.

Keywords: data fusion; equivalency analysis; model accuracy evaluation; critical sample size threshold evaluation; method selection paradigm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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