Independent Component Analysis (ICA) is, in a sense, complementary to singular value decomposition. The factors that an SVD selects are uncorrelated; the starting point for ICA is the stronger assumption that the factors are statistically independent. In order for a factorization like ICA to be possible, all but one of the distributions of the objects along the axes corresponding to the factors must be non-Gaussian.