A supervised variational autoencoder framework for dimensionality reduction and predictive modeling in high-dimensional socioeconomic data

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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Bibliographic Details
Main Authors: Pei Xue, Tianshun Li
Format: Article
Language:English
Published: KeAi Communications Co., Ltd. 2026-01-01
Series:Journal of Economy and Technology
Subjects:
Dimensionality reduction
Supervised variational autoencoder
Nonlinear representation learning
High-dimensional data
Socioeconomic forecasting
Predictive modeling
Online Access:http://www.sciencedirect.com/science/article/pii/S2949948825000204
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http://www.sciencedirect.com/science/article/pii/S2949948825000204

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