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Table 1 Comparison between different ICA algorithms

From: MetICA: independent component analysis for high-resolution mass-spectrometry based non-targeted metabolomics

  MetICA FastICA FastICA icapca icamix kernel-ICA Bayesian
18 Clusters 'Parallel' 'Deflation'    Gaussian
[1] Variance Explained 90 % 90 % 90 % 75.7 % 99 % 99 % 99 %
[2] Component Extracted 18 20 20 7 43 43 9
[3] Maximal Kurtosis 44.1 43.9 44.1 44.1 43.6 3.9 29.8
[4] Minimal abs(Kurtosis) 1.6 3.4 1.9 0.5 15.1 0.008 0.007
[5] Minimal Kurtosis −1.6 3.4 −2 0.5 15.1 −1.7 −0.9
[6] Stable Components 12/18 20/20 9/20 3/7 43/43 0/43 1/9
[7] Model Selection HCA - - LOO-CV Likelihood - BIC
[8] Component Order Bootstrap - Deflation Variance - Deflation Kurtosis
  1. Seven ICA algorithms were compared based on [1] maximal percentage of variance the algorithm could handle (depending on the computer memory), [2] optimal number of components that the algorithm suggests, [3] kurtosis of the most super-Gaussian component [4] kurtosis of the most Gaussian component, [5] minimal kurtosis of components (the most sub-Gaussian when it is negative), [6] number of consistent components extracted in all 10 algorithms runs with an absolute Spearman's correlation between them higher than 0.8 and on whether the algorithm suggests [7] model selection criteria [8] importance order of components