Variational Wasserstein Clustering
By: Researcher
Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).
Please contact us in case of a broken link from here
| Authors | Yalin Wang, Xianfeng Gu, Wen Zhang, Liang Mi |
| Journal/Conference Name | ECCV 2018 9 |
| Paper Category | Artificial Intelligence |
| Paper Abstract | We propose a new clustering method based on optimal transportation. We solve optimal transportation with variational principles, and investigate the use of power diagrams as transportation plans for aggregating arbitrary domains into a fixed number of clusters. We iteratively drive centroids through target domains while maintaining the minimum clustering energy by adjusting the power diagrams. Thus, we simultaneously pursue clustering and the Wasserstein distances between the centroids and the target domains, resulting in a measure-preserving mapping. We demonstrate the use of our method in domain adaptation, remeshing, and representation learning on synthetic and real data. |
| Date of publication | 2018 |
| Code Programming Language | Multiple |
| Comment |
Copyright Researcher 2022