Extreme Learning Machine for the Characterization of Anomalous Diffusion from Single Trajectories (AnDi-ELM)

TL;DR

This paper introduces AnDi-ELM, a simple and fast machine learning approach using extreme learning machines and feature engineering to characterize anomalous diffusion from single trajectories, suitable for quick screening.

Contribution

The paper presents a novel, efficient method combining extreme learning machine and feature engineering for anomalous diffusion analysis, outperforming traditional approaches in speed and simplicity.

Findings

Achieves satisfactory performance on AnDi challenge tasks

Offers fast training with limited computing resources

Suitable for preliminary screening of anomalous diffusion

Abstract

The study of the dynamics of natural and artificial systems has provided several examples of deviations from Brownian behavior, generally defined as anomalous diffusion. The investigation of these dynamics can provide a better understanding of diffusing objects and their surrounding media, but a quantitative characterization from individual trajectories is often challenging. Efforts devoted to improving anomalous diffusion detection using classical statistics and machine learning have produced several new methods. Recently, the anomalous diffusion challenge (AnDi, www.andi-challenge.org) was launched to objectively assess these approaches on a common dataset, focusing on three aspects of anomalous diffusion: the inference of the anomalous diffusion exponent; the classification of the diffusion model; and the segmentation of trajectories. In this article, I describe a simple approach to tackle the tasks of the AnDi challenge by combining extreme learning machine and feature engineering (AnDi-ELM). The method reaches satisfactory performance while offering a straightforward implementation and fast training time with limited computing resources, making it a suitable tool for fast preliminary screening of anomalous diffusion.