A Systematic Framework for Tabular Data Disentanglement

TL;DR

This paper introduces a systematic framework for disentangling complex interrelationships in tabular data, aiming to improve data processing and synthesis by modularizing the process into four core components.

Contribution

It proposes a modular framework that structures tabular data disentanglement into four key components, enhancing understanding and guiding future research.

Findings

Framework provides a systematic view of tabular data disentanglement.

Case study demonstrates the framework's applicability in synthetic data generation.

Lays foundation for developing scalable and robust disentanglement methods.

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.