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

ZHENG Dezhong, YANG Yuanyuan, XIE Zhe, NI Yangfan, LI Wentao
2021 Shanghai Jiaotong Daxue xuebao  
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
more » ... 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.
doi:10.16183/j.cnki.jsjtu.2020.082 doaj:b3062be502e4437dade77db9156d233a fatcat:ruh7klnp4jf5dgai75xkkx65s4