Journal of Shanghai Jiao Tong University

Journal of Shanghai Jiaotong University >

2021 , Vol. 55 >Issue 2: 131 - 140

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2020.082

Data Splitting Method of Distance Metric Learning Based on Gaussian Mixed Model

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  • 1.Laboratory for Medical Imaging Informatics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200080, China
    2.University of Chinese Academy of Sciences, Beijing 100049, China
    3.Fudan University Shanghai Cancer Center, Shanghai 200032, China

Received date: 2020-03-24

  Online published: 2021-03-03

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Abstract

Aimed at the problem of instability and deviation of multiple training model in limited samples, this paper proposes a method of distance metric learning based on the Gaussian mixture model, which can solve this problem more reasonably by dividing the dataset. Distance metric learning relies on the excellent feature extraction capabilities of deep neural networks to embed the original data into the new metric space. Then, based on the deep features, the Gaussian mixture model is used to cluster the analyzer and estimate the sample distribution in this new metric space. Finally, according to the characteristics of sample distribution, stratified sampling is used to reasonably divide the data. The research shows that the method proposed can better understand the characteristics of data distribution and obtain a more reasonable data division, thereby improving the accuracy and generalization of the model.

Key words： artificial intelligence training; dataset division; deep neural networks; Gaussian mixture model

Cite this article

ZHENG Dezhong, YANG Yuanyuan, XIE Zhe, NI Yangfan, LI Wentao . Data Splitting Method of Distance Metric Learning Based on Gaussian Mixed Model[J]. Journal of Shanghai Jiaotong University, 2021 , 55(2) : 131 -140 . DOI: 10.16183/j.cnki.jsjtu.2020.082

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