Schema matching using Gaussian mixture models with Wasserstein distance

2021/11/28 by Mateusz Przyborowski, M. Pabis, Przyborowski, Mateusz +5
Computer Science · #Anomaly Detection Techniques and Applications #Bayesian Methods and Mixture Models #Databases (cs.DB) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Video Surveillance and Tracking Methods

paper · pdf · doi:10.48550/arxiv.2111.14244

openalex publication_date 2021/11/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Gaussian mixture models find their place as a powerful tool, mostly in the clustering problem, but with proper preparation also in feature extraction, pattern recognition, image segmentation and in general machine learning. When faced with the problem of schema matching, different mixture models computed on different pieces of data can maintain crucial information about the structure of the dataset. In order to measure or compare results from mixture models, the Wasserstein distance can be very useful, however it is not easy to calculate for mixture distributions. In this paper we derive one of possible approximations for the Wasserstein distance between Gaussian mixture models and reduce it to linear problem. Furthermore, application examples concerning real world data are shown.

Citations

• Cone structure of L²-Wasserstein spaces

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