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- W4382173539 abstract "This contribution presents significant computational accelerations to prominent schemes, which enable the comparison of measures, even with varying masses. Concisely, we employ nonequispaced fast Fourier transform to accelerate the radial kernel convolution in unbalanced optimal transport approximation, building on the Sinkhorn algorithm. Accelerated schemes are presented as well for the maximum mean discrepancies involving kernels based on distances. By employing nonequispaced fast Fourier transform, our approaches significantly reduce the arithmetic operations to compute the distances from $mathcal O(n^2)$ to $mathcal O(nlog n)$, which enables access to large and high-dimensional data sets. Furthermore, we show some robust relation between the Wasserstein distance and maximum mean discrepancies. Numerical experiments using synthetic data and real datasets demonstrate the computational acceleration and numerical precision." @default.
- W4382173539 created "2023-06-27" @default.
- W4382173539 creator A5028943473 @default.
- W4382173539 creator A5091299788 @default.
- W4382173539 date "2023-06-23" @default.
- W4382173539 modified "2023-10-12" @default.
- W4382173539 title "Fast Approximation of Unbalanced Optimal Transport and Maximum Mean Discrepancies" @default.
- W4382173539 doi "https://doi.org/10.48550/arxiv.2306.13618" @default.
- W4382173539 hasPublicationYear "2023" @default.
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