Independent Component Analysis Based on Chatterjee's Correlation Coefficient | ||
| Journal of Artificial Intelligence and Computational Theory | ||
| Articles in Press, Accepted Manuscript, Available Online from 24 August 2026 PDF (1.59 M) | ||
| Document Type: Original Article | ||
| Author | ||
| Hossein Nadeb* | ||
| Department of Statistics, Yazd University, Yazd, Iran | ||
| Abstract | ||
| In conventional Independent Component Analysis (ICA) algorithms, the objective function is typically defined based on specific dependency criteria. The choice of these criteria is not merely a technical detail; it fundamentally influences the algorithm's convergence speed, robustness to outliers, and ability to separate sources under non-ideal conditions such as limited sample sizes or noisy mixtures. This article introduces a novel ICA algorithm that uses Chatterjee's correlation coefficient as the contrast function. The performance of the proposed method is evaluated and compared against two recent ICA algorithms, using the Amari error as a quantitative benchmark across multiple simulation scenarios. Furthermore, to demonstrate its practical utility, the proposed algorithm is applied to real-world time series data as a pre-processing step for clustering. | ||
| Keywords | ||
| Amari error; Clustering; Monte Carlo simulation; Time series | ||
| Full Text | ||
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