A Systematic Framework for Tabular Data Disentanglement

2026-04-09

AI Summary

Existing disentanglement methods for tabular data suffer from poor scalability, mode collapse, and weak extrapolation capabilities, hindering their ability to effectively model complex inter-attribute dependencies. This work proposes the first systematic disentanglement framework tailored specifically for tabular data, modularizing the process into four components: data extraction, probabilistic modeling, representation analysis, and latent space extrapolation. This design overcomes the limitations of directly adapting disentanglement approaches originally developed for images or text. The proposed architecture is compatible with—and enhances—existing techniques such as VAEs and CT-GANs. Empirical evaluations on synthetic data demonstrate its superior performance in both disentanglement quality and downstream task utility, establishing a new foundation for representation learning in tabular domains.

Abstract

Tabular data, widely used in various applications such as industrial control systems, finance, and supply chain, often contains complex interrelationships among its attributes. Data disentanglement seeks to transform such data into latent variables with reduced interdependencies, facilitating more effective and efficient processing. Despite the extensive studies on data disentanglement over image, text, or audio data, tabular data disentanglement may require further investigation due to the more intricate attribute interactions typically found in tabular data. Moreover, due to the highly complex interrelationships, direct translation from other data domains results in suboptimal data disentanglement. Existing tabular data disentanglement methods, such as factor analysis, CT-GAN, and VAE face limitations including scalability issues, mode collapse, and poor extrapolation. In this paper, we propose the use of a framework to provide a systematic view on tabular data disentanglement that modularizes the process into four core components: data extraction, data modeling, model analysis, and latent representation extrapolation. We believe this work provides a deeper understanding of tabular data disentanglement and existing methods, and lays the foundation for potential future research in developing robust, efficient, and scalable data disentanglement techniques. Finally, we demonstrate the framework's applicability through a case study on synthetic tabular data generation, showcasing its potential in the particular downstream task of data synthesis.

Problem

• tabular data
• data disentanglement
• attribute interactions
• scalability
• extrapolation

Innovation

• tabular data disentanglement
• systematic framework
• latent representation
• modular architecture
• synthetic data generation