A supervised variational autoencoder framework for dimensionality reduction and predictive modeling in high-dimensional socioeconomic data
We introduce an estimation framework utilizing a Supervised Variational Autoencoder (SVAE) to address challenges posed by high-dimensional socioeconomic data. Unlike classical linear dimensionality reduction methods, such as PCA and Lasso regression, the proposed SVAE effectively captures complex no...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2026-01-01
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| Series: | Journal of Economy and Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2949948825000204 |
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