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 rate of T^1/2 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.
Users
Please
log in to take part in the discussion (add own reviews or comments).