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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 2/2023

17-09-2022 | Original Article

A novel adaptive methodology for removing spurious components in a modified incremental Gaussian mixture model

Authors: Shuping Sun, Yaonan Tong, Biqiang Zhang, Bowen Yang, Long Yan, Peiguang He, Hong Xu

Published in: International Journal of Machine Learning and Cybernetics | Issue 2/2023

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Abstract

Regarding the computational complexity of the update procedure in the fast incremental Gaussian mixture model (FIGMM) and no efficiency for removing the spurious component in the incremental Gaussian mixture model (IGMM), this study proposes a novel algorithm called the modified incremental Gaussian mixture model (MIGMM) which is an improvement of FIGMM, and a novel adaptive methodology for removing spurious components in the MIGMM. The major contributions in this study are twofold. Firstly, a more simple and efficient prediction matrix update, which is the core of the update procedure in the MIGMM algorithm, is proposed compared to that described in FIGMM. Secondly, an effective exponential model (\(p_{\mathrm {_{Thv}}}\)) related to the number of output components generated in MIGMM, combined with the Mahalanobis distance-based logical matrix (LM), is proposed to remove spurious components and determine the correct components. Based on the highlighted contributions, regarding the removal of spurious components, comparative experiments studied on synthetic and real data sets show that the proposed framework performs robustly compared with other famous information criteria used to determine the number of components. The performance evaluation of IGMM compared with other efficient unsupervised algorithms is verified by conducting on both synthetic and real-world data sets.
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Metadata
Title
A novel adaptive methodology for removing spurious components in a modified incremental Gaussian mixture model
Authors
Shuping Sun
Yaonan Tong
Biqiang Zhang
Bowen Yang
Long Yan
Peiguang He
Hong Xu
Publication date
17-09-2022
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 2/2023
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-022-01649-w

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