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# Deterministic Independent Component Analysis

*Proceedings of the 32nd International Conference on Machine Learning*, PMLR 37:2521-2530, 2015.

#### Abstract

We study independent component analysis with noisy observations. We present, for the first time in the literature, consistent, polynomial-time algorithms to recover non-Gaussian source signals and the mixing matrix with a reconstruction error that vanishes at a 1/\sqrtT rate using T observations and scales only polynomially with the natural parameters of the problem. Our algorithms and analysis also extend to deterministic source signals whose empirical distributions are approximately independent.