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A Mode-Partitioned Gamma Mixture Model Estimation Method for Large-Scale Multimodal Data

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Bibliographic Details
Title: A Mode-Partitioned Gamma Mixture Model Estimation Method for Large-Scale Multimodal Data
Authors: Jiaqi Chen, Yuyang Xu, Xu Li, Muhammad Azhar
Source: Big Data Mining and Analytics, Vol 9, Iss 1, Pp 4-22 (2026)
Publisher Information: Tsinghua University Press, 2026.
Publication Year: 2026
Collection: LCC:Electronic computers. Computer science
Subject Terms: multimodal distribution, gamma mixture modell (gamm), clustering, big data, probability density function (pdf), Electronic computers. Computer science, QA75.5-76.95
Description: Gamma Mixture Model (GaMM) is a useful tool for representing complex distributions. However, estimating the parameters of GaMM faces challenges due to the lack of closed-form solution for the shape parameter. Existing parameter estimation methods face limitations stemming from their reliance on approximate computations, which degrade estimation accuracy, as well as the inherent complexity of numerical calculations, leading to computational inefficiency. To address these limitations and fully consider the multimodal nature of big data, this paper proposes a Mode-Partitioned GaMM (MP-GaMM) estimation method for large-scale multimodal data. The MP-GaMM method explores the spatial distribution characteristics of the data through clustering to partition the data into distinct modes, addresses mode overlap with a tune-up strategy, and employs closed-form estimator for parameter estimation of each mode in parallel. Experimental results demonstrate the rationality and effectiveness of the proposed MP-GaMM method, which outperforms existing methods in both accuracy and computational efficiency. Specifically, MP-GaMM exhibits lower error metrics, higher log-likelihood values and shorter runtime, indicating its capability to provide a more accurate estimation of the model parameters, and more precise characterization of the multimodal nature of large-scale data.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 2096-0654
2097-406X
Relation: https://www.sciopen.com/article/10.26599/BDMA.2025.9020045; https://doaj.org/toc/2096-0654; https://doaj.org/toc/2097-406X
DOI: 10.26599/BDMA.2025.9020045
Access URL: https://doaj.org/article/bb51284672d645ef9d82dcf6bded94bc
Accession Number: edsdoj.bb51284672d645ef9d82dcf6bded94bc
Database: Directory of Open Access Journals
Description
Abstract:Gamma Mixture Model (GaMM) is a useful tool for representing complex distributions. However, estimating the parameters of GaMM faces challenges due to the lack of closed-form solution for the shape parameter. Existing parameter estimation methods face limitations stemming from their reliance on approximate computations, which degrade estimation accuracy, as well as the inherent complexity of numerical calculations, leading to computational inefficiency. To address these limitations and fully consider the multimodal nature of big data, this paper proposes a Mode-Partitioned GaMM (MP-GaMM) estimation method for large-scale multimodal data. The MP-GaMM method explores the spatial distribution characteristics of the data through clustering to partition the data into distinct modes, addresses mode overlap with a tune-up strategy, and employs closed-form estimator for parameter estimation of each mode in parallel. Experimental results demonstrate the rationality and effectiveness of the proposed MP-GaMM method, which outperforms existing methods in both accuracy and computational efficiency. Specifically, MP-GaMM exhibits lower error metrics, higher log-likelihood values and shorter runtime, indicating its capability to provide a more accurate estimation of the model parameters, and more precise characterization of the multimodal nature of large-scale data.
ISSN:20960654
2097406X
DOI:10.26599/BDMA.2025.9020045