Unraveling Complexity: Singular Value Decomposition in Complex Experimental Data Analysis
Data Analysis, Statistics and Probability
2024-07-24 v1 Disordered Systems and Neural Networks
Mesoscale and Nanoscale Physics
Abstract
Analyzing complex experimental data with multiple parameters is challenging. We propose using Singular Value Decomposition (SVD) as an effective solution. This method, demonstrated through real experimental data analysis, surpasses conventional approaches in understanding complex physics data. Singular values and vectors distinguish and highlight various physical mechanisms and scales, revealing previously challenging elements. SVD emerges as a powerful tool for navigating complex experimental landscapes, showing promise for diverse experimental measurements.
Cite
@article{arxiv.2407.16267,
title = {Unraveling Complexity: Singular Value Decomposition in Complex Experimental Data Analysis},
author = {Judith F. Stein and Aviad Frydman and Richard Berkovits},
journal= {arXiv preprint arXiv:2407.16267},
year = {2024}
}
Comments
11 pages, 4 figures, submitted to SciPost Physics Core