A Comparative Analysis of Three Data Fusion Methods and Construction of the Fusion Method Selection Paradigm
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 t...
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| Format: | Article |
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MDPI AG
2025-04-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/8/1218 |
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| author | Ziqi Liu Ziqiao Yin Zhilong Mi Binghui Guo Zhiming Zheng |
| author_facet | Ziqi Liu Ziqiao Yin Zhilong Mi Binghui Guo Zhiming Zheng |
| author_sort | Ziqi Liu |
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| description | 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. |
| format | Article |
| id | doaj-art-e8a03daa3b6a44929a37ef873e2eec1a |
| institution | OA Journals |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-e8a03daa3b6a44929a37ef873e2eec1a2025-08-20T02:28:15ZengMDPI AGMathematics2227-73902025-04-01138121810.3390/math13081218A Comparative Analysis of Three Data Fusion Methods and Construction of the Fusion Method Selection ParadigmZiqi Liu0Ziqiao Yin1Zhilong Mi2Binghui Guo3Zhiming Zheng4School of Mathematical Sciences, Beihang University, Beijing 100191, ChinaKey Laboratory of Mathematics, Informatics and Behavioral Semantics and State, Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, ChinaKey Laboratory of Mathematics, Informatics and Behavioral Semantics and State, Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, ChinaKey Laboratory of Mathematics, Informatics and Behavioral Semantics and State, Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, ChinaKey Laboratory of Mathematics, Informatics and Behavioral Semantics and State, Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, ChinaMultisource 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.https://www.mdpi.com/2227-7390/13/8/1218data fusionequivalency analysismodel accuracy evaluationcritical sample size threshold evaluationmethod selection paradigm |
| spellingShingle | Ziqi Liu Ziqiao Yin Zhilong Mi Binghui Guo Zhiming Zheng A Comparative Analysis of Three Data Fusion Methods and Construction of the Fusion Method Selection Paradigm Mathematics data fusion equivalency analysis model accuracy evaluation critical sample size threshold evaluation method selection paradigm |
| title | A Comparative Analysis of Three Data Fusion Methods and Construction of the Fusion Method Selection Paradigm |
| title_full | A Comparative Analysis of Three Data Fusion Methods and Construction of the Fusion Method Selection Paradigm |
| title_fullStr | A Comparative Analysis of Three Data Fusion Methods and Construction of the Fusion Method Selection Paradigm |
| title_full_unstemmed | A Comparative Analysis of Three Data Fusion Methods and Construction of the Fusion Method Selection Paradigm |
| title_short | A Comparative Analysis of Three Data Fusion Methods and Construction of the Fusion Method Selection Paradigm |
| title_sort | comparative analysis of three data fusion methods and construction of the fusion method selection paradigm |
| topic | data fusion equivalency analysis model accuracy evaluation critical sample size threshold evaluation method selection paradigm |
| url | https://www.mdpi.com/2227-7390/13/8/1218 |
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