Tabular comparison of the limitations of different ICA algorithms
A tabular comparison of the limitations of different ICA algorithms : ICA Algorithm Key Limitations When to Avoid FastICA - Assumes linear mixing - Sensitive to outliers - Requires non-Gaussian sources Noisy data, Gaussian-like signals Infomax ICA - Slow convergence - May get stuck in local optima - Needs tuning of learning rate Large datasets, real-time applications JADE (Joint Approx. Diagonalization) - Computationally expensive (O(n³)) - Struggles with high-dimensional data Big data (e.g., 4D fMRI), low-resource systems Robust ICA (R-FastICA) - Still assumes near-linear mixing - Complex parameter tuning Strongly nonlinear mixtures DeepICA (Neural ICA) - Requires large datasets - Black-box nature (less interpretable) - High comp...