Entropic Gromov-Wasserstein between Gaussian Distributions

被引:0
作者
Khang Le [1 ]
Dung Le [2 ]
Huy Nguyen [3 ]
Dat Do [4 ]
Tung Pham [3 ]
Nhat Ho [1 ]
机构
[1] Univ Texas Austin, Austin, TX 78712 USA
[2] Ecole Polytech, Palaiseau, France
[3] VinAI Res, Hanoi, Vietnam
[4] Univ Michigan, Ann Arbor, MI USA
来源
INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 162 | 2022年
关键词
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中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We study the entropic Gromov-Wasserstein and its unbalanced version between (unbalanced) Gaussian distributions with different dimensions. When the metric is the inner product, which we refer to as inner product Gromov-Wasserstein (IGW), we demonstrate that the optimal transportation plans of entropic IGW and its unbalanced variant are (unbalanced) Gaussian distributions. Via an application of von Neumann's trace inequality, we obtain closed-form expressions for the entropic IGW between these Gaussian distributions. Finally, we consider an entropic inner product Gromov-Wasserstein barycenter of multiple Gaussian distributions. We prove that the barycenter is a Gaussian distribution when the entropic regularization parameter is small. We further derive a closed-form expression for the covariance matrix of the barycenter.
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页数:40
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