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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Veröffentlicht in:	Journal of Economy and Technology Jg. 4; S. 9 - 19
Hauptverfasser:	Xue, Pei, Li, Tianshun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 2026 KeAi Communications Co., Ltd
Schlagworte:	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-Zugang:	Volltext
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