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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Journal of Shanghai Jiao Tong University
2021-02-01
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| Series: | Shanghai Jiaotong Daxue xuebao |
| Subjects: | |
| Online Access: | http://xuebao.sjtu.edu.cn/CN/10.16183/j.cnki.jsjtu.2020.082 |
| _version_ | 1819012655543746560 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-21T01:47:30Z |
| format | Article |
| id | doaj.art-b3062be502e4437dade77db9156d233a |
| institution | Directory Open Access Journal |
| issn | 1006-2467 |
| language | zho |
| last_indexed | 2024-12-21T01:47:30Z |
| publishDate | 2021-02-01 |
| publisher | Editorial Office of Journal of Shanghai Jiao Tong University |
| record_format | Article |
| series | Shanghai Jiaotong Daxue xuebao |
| spelling | doaj.art-b3062be502e4437dade77db9156d233a2022-12-21T19:19:59ZzhoEditorial Office of Journal of Shanghai Jiao Tong UniversityShanghai Jiaotong Daxue xuebao1006-24672021-02-01550213114010.16183/j.cnki.jsjtu.2020.0821006-2467(2021)02-0131-10Data Splitting Method of Distance Metric Learning Based on Gaussian Mixed ModelZHENG Dezhong0YANG Yuanyuan1XIE Zhe2NI Yangfan3LI Wentao4Laboratory for Medical Imaging Informatics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200080, ChinaLaboratory for Medical Imaging Informatics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200080, ChinaLaboratory for Medical Imaging Informatics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200080, ChinaLaboratory for Medical Imaging Informatics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200080, ChinaFudan University Shanghai Cancer Center, Shanghai 200032, ChinaAimed 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.http://xuebao.sjtu.edu.cn/CN/10.16183/j.cnki.jsjtu.2020.082artificial intelligence trainingdataset divisiondeep neural networksgaussian mixture model |
| spellingShingle | ZHENG Dezhong YANG Yuanyuan XIE Zhe NI Yangfan LI Wentao Data Splitting Method of Distance Metric Learning Based on Gaussian Mixed Model Shanghai Jiaotong Daxue xuebao artificial intelligence training dataset division deep neural networks gaussian mixture model |
| title | 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_short | 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 |
| topic | artificial intelligence training dataset division deep neural networks gaussian mixture model |
| url | http://xuebao.sjtu.edu.cn/CN/10.16183/j.cnki.jsjtu.2020.082 |
| work_keys_str_mv | AT zhengdezhong datasplittingmethodofdistancemetriclearningbasedongaussianmixedmodel AT yangyuanyuan datasplittingmethodofdistancemetriclearningbasedongaussianmixedmodel AT xiezhe datasplittingmethodofdistancemetriclearningbasedongaussianmixedmodel AT niyangfan datasplittingmethodofdistancemetriclearningbasedongaussianmixedmodel AT liwentao datasplittingmethodofdistancemetriclearningbasedongaussianmixedmodel |