Curvature-driven manifold fitting under unbounded isotropic noise

Manifold fitting aims to reconstruct a low-dimensional manifold from high-dimensional data, whose framework is established by Fefferman et al. \cite{fefferman2020reconstruction,fefferman2021reconstruction}. This paper studies the recovery of a compact $C^3$ submanifold $\mathcal{M} \subset \mathbb{R}^D$ with dimension $d<D$ and positive reach $τ$ from observations $Y = X + ξ$, where $X$ is uniformly distributed on $\mathcal{M}$ and $ξ\sim \mathcal{N}(0, σ^2 I_D)$ denotes isotropic Gaussian noise. To project any points $z$ in a tubular neighborhood $Γ$ of $\mathcal{M}$ onto $\mathcal{M}$, we construct a sample-based estimator $F:Γ\to\mathbb{R}^D$ by a normalized local kernel with the theoretically derived bandwidth $r = c_Dσ$. Under a sample size of $O(σ^{-3d-5})$, we establish with high probability the uniform asymptotic expansion $F(z) = π(z) + \frac{d}{2} H_{π(z)} σ^2 + O(σ^3), \qquad z \in Γ,$ where $π(z)$ is the projection of $z$ onto $\mathcal{M}$ and $H_{π(z)}$ is the mean curvature vector of $\mathcal{M}$ at $π(z)$. The resulting manifold $F(Γ)$ has reach bounded below by $c τ$ for $c>0$ and achieves a state-of-the-art Hausdorff distance of $O(σ^2)$ to $\mathcal{M}$. Numerical experiments confirm the quadratic decay of the reconstruction error and demonstrate the computational efficiency of the estimator $F$. Our work provides a curvature-driven framework for denoising and reconstructing manifolds with second-order accuracy.

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