Schema matching using Gaussian mixture models with Wasserstein distance
- URL: http://arxiv.org/abs/2111.14244v1
- Date: Sun, 28 Nov 2021 21:44:58 GMT
- Title: Schema matching using Gaussian mixture models with Wasserstein distance
- Authors: Mateusz Przyborowski, Mateusz Pabi\'s, Andrzej Janusz, Dominik
\'Sl\k{e}zak
- Abstract summary: We derive approximations for the Wasserstein distance between Gaussian mixture models and reduce it to linear problem.
In this paper we derive one of possible approximations for the Wasserstein distance between Gaussian mixture models and reduce it to linear problem.
- Score: 0.2676349883103403
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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