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原文摘要

Persistence diagrams do not naturally live in a Hilbert space. Carrière, Cuturi, and Oudot embedded them in kernel methods through the Sliced Wasserstein kernel, built from the Sliced Wasserstein distance $\mathrm{SW}_1$, which is conditionally negative definite (CND). We study the projected ($\mathrm{SW}_p$) and reflected ($\mathrm{RW}_p$) Sliced Wasserstein distances for every order $p\ge1$, and compare them with the $p$-Wasserstein distance $d_p$ and with a reflected variant $d_p^r$, in which projection onto the diagonal is replaced by reflection across it. We show that $2^{1/p}d_p\le d_p^r\le 2d_p$ with sharp constants, so that $d_1^r=2d_1$ and $d_p^r$ inherits the stability of $d_p$, although it is a metric only for $p=1$. The distances $\mathrm{SW}_1$ and $\mathrm{RW}_1$ are CND metrics, and $\mathrm{RW}_1$ gives rise to a new positive definite kernel, stable with respect to $d_1$. For $p>1$, neither $\mathrm{SW}_p$ nor $\mathrm{RW}_p$ satisfies the triangle inequality, and rigorous interval-arithmetic certificates prove that $\mathrm{SW}_2$ and $\mathrm{RW}_2$ are not CND; numerical experiments suggest that the CND property fails for every $p$ above some $p^*\in(1,1.2]$. On diagrams with at most $N$ points, $\mathrm{SW}_p$ and $\mathrm{RW}_p$ are metrically equivalent to $d_p$ and $d_p^r$, with sharp upper constants. On an orbit recognition benchmark, the kernels built from $\mathrm{SW}_1$ and $\mathrm{RW}_1$ reach about 85% accuracy, outperform the persistence scale space kernel, perform on par with the persistence weighted Gaussian kernel, and are about one percentage point below the kernel built from $\mathrm{SW}_2$, which is not positive definite.