Results 31 to 40 of about 4,469 (193)
Supervised Tree-Wasserstein Distance
CoRR, 2021 To measure the similarity of documents, the Wasserstein distance is a powerful tool, but it requires a high computational cost. Recently, for fast computation of the Wasserstein distance, methods for approximating the Wasserstein distance using a tree metric have been proposed.
Yuki Takezawa, Ryoma Sato, Makoto Yamada
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The Quantum Wasserstein Distance of Order 1 [PDF]
IEEE Transactions on Information Theory, 2021 We propose a generalization of the Wasserstein distance of order 1 to the quantum states of $n$ qudits. The proposal recovers the Hamming distance for the vectors of the canonical basis, and more generally the classical Wasserstein distance for quantum states diagonal in the canonical basis.
Giacomo De Palma +3 more
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Irregularity of Distribution in Wasserstein Distance [PDF]
Journal of Fourier Analysis and Applications, 2020 16 ...
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Hierarchical Sliced Wasserstein Distance
CoRR, 2022 Accepted to ICLR 2023, 29 pages, 8 figures, 3 tables,
Khai Nguyen +5 more
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Quasi Manhattan Wasserstein Distance
CoRR, 2023 The Quasi Manhattan Wasserstein Distance (QMWD) is a metric designed to quantify the dissimilarity between two matrices by combining elements of the Wasserstein Distance with specific transformations. It offers improved time and space complexity compared to the Manhattan Wasserstein Distance (MWD) while maintaining accuracy.
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Augmented Sliced Wasserstein Distances
CoRR, 2020 While theoretically appealing, the application of the Wasserstein distance to large-scale machine learning problems has been hampered by its prohibitive computational cost. The sliced Wasserstein distance and its variants improve the computational efficiency through the random projection, yet they suffer from low accuracy if the number of projections ...
Xiongjie Chen +2 more
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Ensemble Riemannian data assimilation over the Wasserstein space [PDF]
Nonlinear Processes in Geophysics, 2021 In this paper, we present an ensemble data assimilation paradigm over a Riemannian manifold equipped with the Wasserstein metric. Unlike the Euclidean distance used in classic data assimilation methodologies, the Wasserstein metric can capture the ...
S. K. Tamang +6 more
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Donsker’s theorem in Wasserstein-1 distance [PDF]
Electronic Communications in Probability, 2020 We compute the Wassertein-1 (or Kolmogorov-Rubinstein) distance between a random walk in $R^d$ and the Brownian motion. The proof is based on a new estimate of the Lipschitz modulus of the solution of the Stein's equation. As an application, we can evaluate the rate of convergence towards the local time at 0 of the Brownian motion.
Coutin, Laure, Decreusefond, Laurent
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Target detection based on generalized Bures–Wasserstein distance
EURASIP Journal on Advances in Signal Processing, 2023 Radar target detection with fewer echo pulses in non-Gaussian clutter background is a challenging problem. In this instance, the conventional detectors using coherent accumulation are not very satisfactory.
Zhizhong Huang, Lin Zheng
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Open-Set Signal Recognition Based on Transformer and Wasserstein Distance
Applied Sciences, 2023 Open-set signal recognition provides a new approach for verifying the robustness of models by introducing novel unknown signal classes into the model testing and breaking the conventional closed-set assumption, which has become very popular in real-world
Wei Zhang +4 more
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