A novel adaptive methodology for removing spurious components in a modified incremental Gaussian mixture model-Reference-Cited by-同舟云学术

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

Published:2022-09-17 Issue:2 Volume:14 Page:551-566
ISSN:1868-8071
Container-title:International Journal of Machine Learning and Cybernetics
language:en
Short-container-title:Int. J. Mach. Learn. & Cyber.

Author:

Sun Shuping^ORCID,Tong Yaonan,Zhang Biqiang,Yang Bowen,Yan Long,He Peiguang,Xu Hong

Funder

Key Project of Hunan Provincial Department of Education

Natural Science Foundation of Hunan Province

University-industrycollaborativeproject

Key Laboratory of Hunan Province

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

https://link.springer.com/content/pdf/10.1007/s13042-022-01649-w.pdf

Reference38 articles.

1. Zhang J, Yin Z, Wang R (2017) Pattern classification of instantaneous cognitive task-load through GMM clustering, Laplacian Eigenmap, and ensemble SVMs. IEEE/ACM Trans Comput Biol Bioinf 14:947–965

2. Li Z, Xia Y, Ji Z, Zhang Y (2017) Brain voxel classification in magnetic resonance images using niche differential evolution based Bayesian inference of variational mixture of Gaussians. Neurocomputing 269:47–57

3. Ortiz-Rosario A, Adeli H, Buford JA (2017) MUSIC-expected maximization gaussian mixture methodology for clustering and detection of task-related neuronal firing rates. Behav Brain Res 317:226–236

4. Davari A, Aptoula E, Yanikoglu B, Maier A, Riess C (2018) GMM-based synthetic samples for classification of hyperspectral images with limited training data. IEEE Geosci Remote Sens Lett 15:942–946

5. Simms LM, Blair B, Ruz J, Wurtz R, Kaplan AD, Glenn A (2018) A pulse discrimination with a Gaussian mixture model on an FPGA. Nucl Inst Methods Phys Res A 900:1–7

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