The paper rigorously proves that Gaussian-free independent sources in a linear ICA model are identifiable up to permutation, scale and translation even with arbitrary additive Gaussian noise.
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Foundations of Independent Component Analysis
The paper rigorously proves that Gaussian-free independent sources in a linear ICA model are identifiable up to permutation, scale and translation even with arbitrary additive Gaussian noise.