A randomized scheme that succeeds with probability $1-\delta$ (for any $\delta>0$) has been devised to construct (1) an equidistributed $\epsilon$-cover of a compact Riemannian symmetric space $\mathbb M$ of dimension $d_{\mathbb M}$ and antipodal dimension $\bar{d}_{\mathbb M}$, and (2) an approximate $(\lambda_r,2)$-design, using $n(\epsilon,\delta)$-many Haar-random isometries of $\mathbb M$, where \begin{equation}n(\epsilon,\delta):=O_{\mathbb M}\left(d_{\mathbb M}\ln \left(\frac 1\epsilon\right)+\log\left(\frac 1\delta\right)\right)\,,\end{equation} and $\lambda_r$ is the $r$-th smallest eigenvalue of the Laplace-Beltrami operator on $\mathbb M$. The $\epsilon$-cover so-produced can be used to compute the integral of 1-Lipschitz functions within additive $\tilde O(\epsilon)$-error, as well as in comparing persistence homology computed from data cloud to that of a hypothetical data cloud sampled from the uniform measure.
翻译:本文提出了一种随机方案,成功率为 $1-\delta$(对于任何 $\delta>0$),用于构造(1)紧 Riemannian 对称空间 $\mathbb{M}$ 的一个等分布的 $\epsilon$-覆盖,其维度为 $d_{\mathbb{M}}$,反极维度为 $\bar{d}_{\mathbb{M}}$,以及(2)使用 $n(\epsilon,\delta)$ 个 Haar 随机等距映射近似 $(\lambda_r,2)$-设计,其中 \begin{equation}n(\epsilon,\delta):=O_{\mathbb{M}}\left(d_{\mathbb{M}}\ln \left(\frac 1\epsilon\right)+\log\left(\frac 1\delta\right)\right)\,,\end{equation} 而 $\lambda_r$ 是 $\mathbb{M}$ 上 Laplace-Beltrami 算子的第 $r$ 小特征值。所产生的 $\epsilon$-覆盖可用于计算 1-Lipschitz 函数的积分,误差为 $\tilde O(\epsilon)$,以及将数据云计算的持久性同调与假设从均匀分布中采样的数据云的持久性同调进行比较。