A Machine Learning Approach to GDP Data Analysis Using Independent Component Analysis (ICA)

Authors

  • Fatemeh Asadi * Department of Statistics, Yazd University, Yazd, Iran.

https://doi.org/10.48314/jidcm.v1i1.61

Abstract

The objective function of numerous well-established Independent Component Analysis (ICA) algorithms, widely recognized as unsupervised machine learning methods, is rooted in specific dependence criteria. This study introduces a distinctive dependence criterion based on the Cumulative Distribution Function (CDF) for characterizing the independence between two random variables. Furthermore, an in-depth exploration of the inherent properties of these variables is conducted. The proposed machine learning algorithms are then applied to real-world time series data, serving as an effective pre-processing clustering method. The algorithm's performance is systematically compared with several previous machine learning-based ICA algorithms.

Keywords:

Amari error, Clustering, Cumulative distribution function, Dependence criteria, Independent components analysis

References

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Published

2025-03-14

Issue

Vol. 1 No. 1 (2025): Journal of Intelligent Decision and Computational Modelling

Section

Articles

How to Cite

Asadi, F. . (2025). A Machine Learning Approach to GDP Data Analysis Using Independent Component Analysis (ICA). Journal of Intelligent Decision and Computational Modelling, 1(1), 50-56. https://doi.org/10.48314/jidcm.v1i1.61