An outlier detection and recovery method based on moving least squares quasi-interpolation scheme and l0-minimization problem

In the scattered data used for function approximation, there may be some data that de-viate greatly from their true values, which are called outliers. The existence of outliers significantly affects the accuracy of some approximation methods, such as moving least squares. How to quickly and accurately find these outliers and restore their true values has not been well resolved. In this paper, a new outlier detection and recovery method is proposed for data processing. The method uses the high accuracy of moving least squares quasi-interpolation scheme, its sensitivity to outliers and the sparse distribution of out-liers to construct a l0-minimization problem with an inequality constrain. Under certain assumptions, it is proved theoretically that the deviation vector corresponding to the out-liers is a solution to the optimization problem. The classical orthogonal matching pursuit algorithm is introduced to solve the optimization problem efficiently. By solving the prob-lem, the outliers are marked, and the deviations are also estimated approximately, which can restore the true values of outliers. The numerical experiments demonstrate that the proposed method has high computational efficiency, very high detection accuracy, and high recovery accuracy for the scattered data used for function approximation, so it is practical. & COPY; 2023 Elsevier Inc. All rights reserved.

Automated summary

This paper proposes a new outlier detection and recovery method for data processing using the high accuracy of moving least squares quasi-interpolation scheme, its sensitivity to outliers, and the sparse distribution of outliers. The method constructs an l0-minimization problem with an inequality constraint and solves it efficiently using the classical orthogonal matching pursuit algorithm. The experiments demonstrate that the proposed method has high computational efficiency, very high detection accuracy, and high recovery accuracy for scattered data used for function approximation, making it practical.