Regularization for the Approximation of 2D Set of Points via the Length of the Curve

TL;DR

This paper introduces a novel regularization method for approximating 2D point sets with piecewise linear functions, deriving an upper bound for the penalty coefficient to control the approximation quality.

Contribution

The study proposes a new regularization approach with a derived upper bound for the penalty coefficient, enhancing control over 2D point set approximations.

Findings

Optimal solution tends to a line as penalty increases

Derived an explicit upper bound for the penalty coefficient

Numerical examples demonstrate the effectiveness of the approach

Abstract

We study the problem of approximation of 2D set of points. Such type of problems always occur in physical experiments, econometrics, data analysis and other areas. The often problems of outliers or spikes usually make researchers to apply regularization techniques, such as Lasso, Ridge or Elastic Net. These approaches always employ penalty coefficient. So the important question of evaluation of the upper bound for the coefficient arises. In the current study we propose a novel way of regularization and derive the upper bound for the used penalty coefficient. First the problem in a general form is stated. The solution is sought in the class of piecewise continuously differentiable functions. It is shown that the optimal solution belongs to the class of piecewise linear functions. So the problem of obtaining the piecewise linear approximation that fits 2D set of point the best is stated. We show that the optimal solution is trivial and tends to a line as penalty coefficient tends to infinity. Then the main result is stated and proved. It provides the upper bound for the penalty coefficient prior to which the optimal solution differs from the line more than some pregiven positive number. We also demonstrate the proposed ideas on numerical examples which include comparison with other regularization approaches.