Feb. 9, 2024, 5:44 a.m. | Keaton Hamm Andrzej Korzeniowski

cs.LG updates on arXiv.org arxiv.org

We expound on some known lower bounds of the quadratic Wasserstein distance between random vectors in $\mathbb{R}^n$ with an emphasis on affine transformations that have been used in manifold learning of data in Wasserstein space. In particular, we give concrete lower bounds for rotated copies of random vectors in $\mathbb{R}^2$ by computing the Bures metric between the covariance matrices. We also derive upper bounds for compositions of affine maps which yield a fruitful variety of diffeomorphisms applied to an initial …

concrete cs.lg data manifold random space stat.ml vectors

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