Average-distance problem for parameterized curves

TL;DR

This paper studies the problem of approximating a measure with a parameterized curve that balances fitting accuracy and length, providing regularity results and conditions for the injectivity of minimizers, linking it to geometric curve problems.

Contribution

It introduces a regularized functional for measure approximation by parameterized curves, proving regularity and injectivity of minimizers, and establishing the geometric nature of the problem.

Findings

Minimizers have bounded total curvature.

Minimizing curves are injective under certain conditions.

The problem is equivalent to minimizing over embedded curves.

Abstract

We consider approximating a measure by a parameterized curve subject to length penalization. That is for a given finite positive compactly supported measure $\mu$, for $p \geq 1$ and $\lambda>0$ we consider the functional \[ E(\gamma) = \int_{\mathbb{R}^d} d(x, \Gamma_\gamma)^p d\mu(x) + \lambda \,\textrm{Length}(\gamma) \] where $\gamma:I \to \mathbb{R}^d$, $I$ is an interval in $\mathbb{R}$, $\Gamma_\gamma = \gamma(I)$, and $d(x, \Gamma_\gamma)$ is the distance of $x$ to $\Gamma_\gamma$. The problem is closely related to the average-distance problem, where the admissible class are the connected sets of finite Hausdorff measure $\mathcal H^1$, and to (regularized) principal curves studied in statistics. We obtain regularity of minimizers in the form of estimates on the total curvature of the minimizers. We prove that for measures $\mu$ supported in two dimensions the minimizing curve is injective if $p \geq 2$ or if $\mu$ has bounded density. This establishes that the minimization over parameterized curves is equivalent to minimizing over embedded curves and thus confirms that the problem has a geometric interpretation.