• Corpus ID: 228064556

# A Riemannian Block Coordinate Descent Method for Computing the Projection Robust Wasserstein Distance

@article{Huang2020ARB,
title={A Riemannian Block Coordinate Descent Method for Computing the Projection Robust Wasserstein Distance},
author={Minhui Huang and Shiqian Ma and Lifeng Lai},
journal={ArXiv},
year={2020},
volume={abs/2012.05199}
}
• Published 9 December 2020
• Computer Science
• ArXiv
The Wasserstein distance has become increasingly important in machine learning and deep learning. Despite its popularity, the Wasserstein distance is hard to approximate because of the curse of dimensionality. A recently proposed approach to alleviate the curse of dimensionality is to project the sampled data from the high dimensional probability distribution onto a lower-dimensional subspace, and then compute the Wasserstein distance between the projected data. However, this approach requires…

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