# Metrics for Inter-Dataset Similarity with Example Applications in Synthetic Data and Feature Selection Evaluation -- Extended Version

arXiv: https://arxiv.org/abs/2501.09591 · HTML: https://mlsanity.com/paper/2501.09591/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 44 references that are in the map.
- First posted on arXiv 16 Jan 2025.

## Builds on (most cited first, 6 of 6)

- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [Statistical Aspects of Wasserstein Distances](https://mlsanity.com/paper/1806.05500/) (2018)
- [DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks](https://mlsanity.com/paper/2110.12884/) (2021)
- [SynthEval: A Framework for Detailed Utility and Privacy Evaluation of Tabular Synthetic Data](https://mlsanity.com/paper/2404.15821/) (2024)
- [A Universal Metric of Dataset Similarity for Cross-silo Federated Learning](https://mlsanity.com/paper/2404.18773/) (2024)
- [SimEx: Express Prediction of Inter-dataset Similarity by a Fleet of Autoencoders](https://mlsanity.com/paper/2001.04893/) (2020)

## Built on by (most cited first, 5 of 5)

- [FSEVAL: Feature Selection Evaluation Toolbox and Dashboard](https://mlsanity.com/paper/2604.18227/) (2026)
- [Randomized PCA Forest for Unsupervised Outlier Detection](https://mlsanity.com/paper/2508.12776/) (2025)
- [Disjoint Generation of Synthetic Data](https://mlsanity.com/paper/2507.19700/) (2025)
- [An Empirical Study of Feature Selection Granularity](https://mlsanity.com/paper/2607.24145/) (2026)
- [Towards Truly Unsupervised Evaluation of Feature Selection -- Extended Version](https://mlsanity.com/paper/2608.12057/) (2026)

## Abstract (arXiv)

Measuring inter-dataset similarity is an important task in machine learning and data mining with various use cases and applications. Existing methods for measuring inter-dataset similarity are computationally expensive, limited, or sensitive to different entities and non-trivial choices for parameters. They also lack a holistic perspective on the entire dataset. In this paper, we propose two novel metrics for measuring inter-dataset similarity. We discuss the mathematical foundation and the theoretical basis of our proposed metrics. We demonstrate the effectiveness of the proposed metrics by investigating two applications in the evaluation of synthetic data and in the evaluation of feature selection methods. The theoretical and empirical studies conducted in this paper illustrate the effectiveness of the proposed metrics.