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...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
ZHENG Dezhong [verfasserIn] YANG Yuanyuan [verfasserIn] XIE Zhe [verfasserIn] NI Yangfan [verfasserIn] LI Wentao [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Chinesisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
In: Shanghai Jiaotong Daxue xuebao - Editorial Office of Journal of Shanghai Jiao Tong University, 2021, 55(2021), 02, Seite 131-140 |
|---|---|
| Übergeordnetes Werk: |
volume:55 ; year:2021 ; number:02 ; pages:131-140 |
| Links: |
|---|
| DOI / URN: |
10.16183/j.cnki.jsjtu.2020.082 |
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| 520 | |a 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. | ||
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10.16183/j.cnki.jsjtu.2020.082 doi (DE-627)DOAJ076309096 (DE-599)DOAJb3062be502e4437dade77db9156d233a DE-627 ger DE-627 rakwb chi TA1-2040 TP155-156 VM1-989 ZHENG Dezhong verfasserin aut Data Splitting Method of Distance Metric Learning Based on Gaussian Mixed Model 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. artificial intelligence training dataset division deep neural networks gaussian mixture model Engineering (General). Civil engineering (General) Chemical engineering Naval architecture. Shipbuilding. Marine engineering YANG Yuanyuan verfasserin aut XIE Zhe verfasserin aut NI Yangfan verfasserin aut LI Wentao verfasserin aut In Shanghai Jiaotong Daxue xuebao Editorial Office of Journal of Shanghai Jiao Tong University, 2021 55(2021), 02, Seite 131-140 (DE-627)1680950819 10062467 nnns volume:55 year:2021 number:02 pages:131-140 https://doi.org/10.16183/j.cnki.jsjtu.2020.082 kostenfrei https://doaj.org/article/b3062be502e4437dade77db9156d233a kostenfrei http://xuebao.sjtu.edu.cn/CN/10.16183/j.cnki.jsjtu.2020.082 kostenfrei https://doaj.org/toc/1006-2467 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_2817 AR 55 2021 02 131-140 |
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Data Splitting Method of Distance Metric Learning Based on Gaussian Mixed Model |
| 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. |
| abstractGer |
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. |
| abstract_unstemmed |
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. |
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| container_issue |
02 |
| title_short |
Data Splitting Method of Distance Metric Learning Based on Gaussian Mixed Model |
| url |
https://doi.org/10.16183/j.cnki.jsjtu.2020.082 https://doaj.org/article/b3062be502e4437dade77db9156d233a http://xuebao.sjtu.edu.cn/CN/10.16183/j.cnki.jsjtu.2020.082 https://doaj.org/toc/1006-2467 |
| remote_bool |
true |
| author2 |
YANG Yuanyuan XIE Zhe NI Yangfan LI Wentao |
| author2Str |
YANG Yuanyuan XIE Zhe NI Yangfan LI Wentao |
| ppnlink |
1680950819 |
| callnumber-subject |
TA - General and Civil Engineering |
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c |
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| doi_str |
10.16183/j.cnki.jsjtu.2020.082 |
| callnumber-a |
TA1-2040 |
| up_date |
2024-07-03T19:46:27.475Z |
| _version_ |
1803588455604682752 |
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7.4019165 |