Schema matching using Gaussian mixture models with Wasserstein distance
This work addresses schema matching, a data integration challenge, with an incremental improvement in computational efficiency for mixture model comparison.
The paper tackles the problem of schema matching by developing an approximation for the Wasserstein distance between Gaussian mixture models, reducing it to a linear problem, and demonstrates its application on real-world data.
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.