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

Published in:	Journal of Economy and Technology Vol. 4; pp. 9 - 19
Main Authors:	Xue, Pei, Li, Tianshun
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 2026 KeAi Communications Co., Ltd
Subjects:	Dimensionality reduction High-dimensional data Nonlinear representation learning Predictive modeling Socioeconomic forecasting Supervised variational autoencoder Dimensionality reduction High-dimensional data Socioeconomic forecasting Predictive modeling Nonlinear representation learning Supervised variational autoencoder
ISSN:	2949-9488, 2949-9488
Online Access:	Get full text
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