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

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...

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Autores principales:	ZHENG Dezhong, YANG Yuanyuan, XIE Zhe, NI Yangfan, LI Wentao
Formato:	article
Lenguaje:	ZH
Publicado:	Editorial Office of Journal of Shanghai Jiao Tong University 2021
Materias:	artificial intelligence training dataset division deep neural networks gaussian mixture model Engineering (General). Civil engineering (General) TA1-2040 Chemical engineering TP155-156 Naval architecture. Shipbuilding. Marine engineering VM1-989
Acceso en línea:	https://doaj.org/article/b3062be502e4437dade77db9156d233a
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id	oai:doaj.org-article:b3062be502e4437dade77db9156d233a
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spelling	oai:doaj.org-article:b3062be502e4437dade77db9156d233a2021-11-04T09:34:25ZData Splitting Method of Distance Metric Learning Based on Gaussian Mixed Model1006-246710.16183/j.cnki.jsjtu.2020.082https://doaj.org/article/b3062be502e4437dade77db9156d233a2021-02-01T00:00:00Zhttp://xuebao.sjtu.edu.cn/CN/10.16183/j.cnki.jsjtu.2020.082https://doaj.org/toc/1006-2467Aimed 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.ZHENG DezhongYANG YuanyuanXIE ZheNI YangfanLI WentaoEditorial Office of Journal of Shanghai Jiao Tong Universityarticleartificial intelligence trainingdataset divisiondeep neural networksgaussian mixture modelEngineering (General). Civil engineering (General)TA1-2040Chemical engineeringTP155-156Naval architecture. Shipbuilding. Marine engineeringVM1-989ZHShanghai Jiaotong Daxue xuebao, Vol 55, Iss 02, Pp 131-140 (2021)
institution	DOAJ
collection	DOAJ
language	ZH
topic	artificial intelligence training dataset division deep neural networks gaussian mixture model Engineering (General). Civil engineering (General) TA1-2040 Chemical engineering TP155-156 Naval architecture. Shipbuilding. Marine engineering VM1-989
spellingShingle	artificial intelligence training dataset division deep neural networks gaussian mixture model Engineering (General). Civil engineering (General) TA1-2040 Chemical engineering TP155-156 Naval architecture. Shipbuilding. Marine engineering VM1-989 ZHENG Dezhong YANG Yuanyuan XIE Zhe NI Yangfan LI Wentao Data Splitting Method of Distance Metric Learning Based on Gaussian Mixed Model
description	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.
format	article
author	ZHENG Dezhong YANG Yuanyuan XIE Zhe NI Yangfan LI Wentao
author_facet	ZHENG Dezhong YANG Yuanyuan XIE Zhe NI Yangfan LI Wentao
author_sort	ZHENG Dezhong
title	Data Splitting Method of Distance Metric Learning Based on Gaussian Mixed Model
title_short	Data Splitting Method of Distance Metric Learning Based on Gaussian Mixed Model
title_full	Data Splitting Method of Distance Metric Learning Based on Gaussian Mixed Model
title_fullStr	Data Splitting Method of Distance Metric Learning Based on Gaussian Mixed Model
title_full_unstemmed	Data Splitting Method of Distance Metric Learning Based on Gaussian Mixed Model
title_sort	data splitting method of distance metric learning based on gaussian mixed model
publisher	Editorial Office of Journal of Shanghai Jiao Tong University
publishDate	2021
url	https://doaj.org/article/b3062be502e4437dade77db9156d233a
work_keys_str_mv	AT zhengdezhong datasplittingmethodofdistancemetriclearningbasedongaussianmixedmodel AT yangyuanyuan datasplittingmethodofdistancemetriclearningbasedongaussianmixedmodel AT xiezhe datasplittingmethodofdistancemetriclearningbasedongaussianmixedmodel AT niyangfan datasplittingmethodofdistancemetriclearningbasedongaussianmixedmodel AT liwentao datasplittingmethodofdistancemetriclearningbasedongaussianmixedmodel
_version_	1718444985884344320

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

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