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

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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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Acknowledgements

This work was supported by the Key Projects of Hunan Provincial Department of Education (Nos. 21A0403 and 21A0405), the Hunan Provincial Natural Science Foundation of China (No. 2022JJ30282), the Key Laboratory of Hunan Province (No. 2019TP1014), and the university-industry collaborative project (No. 202102211006).

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Authors and Affiliations

  1. School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang, 414006, China

    Shuping Sun & Yaonan Tong

  2. Department of Information Engineering, Nanyang Institute of Technology, Nanyang, 473004, China

    Biqiang Zhang, Peiguang He & Hong Xu

  3. School of Integrated Circuits, University of Chinese Academy of Science, Beijing, 101400, China

    Bowen Yang

  4. College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, 518061, China

    Long Yan

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  1. Shuping Sun
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  4. Bowen Yang
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  5. Long Yan
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  6. Peiguang He
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  7. Hong Xu
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Contributions

SS: Conceptualization, Methodology, Data curation, Algorithm design, Software, Writing original draft, Writing review and editing. YT and BZ: Experimental analysis, Investigation, Algorithm validation, Writing review and editing. BY, LY, PH and HX: Data collection, Experimental analysis and editing.

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Correspondence to Shuping Sun.

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Sun, S., Tong, Y., Zhang, B. et al. A novel adaptive methodology for removing spurious components in a modified incremental Gaussian mixture model. Int. J. Mach. Learn. & Cyber. 14, 551–566 (2023). https://doi.org/10.1007/s13042-022-01649-w

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  • DOI: https://doi.org/10.1007/s13042-022-01649-w

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