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: | , , , , |
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| Formato: | article |
| Lenguaje: | ZH |
| Publicado: |
Editorial Office of Journal of Shanghai Jiao Tong University
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/b3062be502e4437dade77db9156d233a |
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| Sumario: | 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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