Beyond Moments: Robustly Learning Affine Transformations with Asymptotically Optimal Error

We present a polynomial-time algorithm for robustly learning an unknown affine transformation of the standard hypercube from samples, an important and well-studied setting for independent component analysis (ICA). Specifically, given an ε-corrupted sample from a distribution D obtained by applying an unknown affine transformation x rightarrow Ax+s to the uniform distribution on a d-dimensional hypercube [-1,1]d, our algorithm constructs hatA, hats such that the total variation distance of the distribution hatD from D is O(ε) using poly(d) time and samples. Total variation distance is the information-theoretically strongest possible notion of distance in our setting and our recovery guarantees in this distance are optimal up to the absolute constant factor multiplying ε. In particular, if the columns of A are normalized to be unit length, our total variation distance guarantee implies a bound on the sum of the ℓ2 distances between the column vectors of A and A', sumi =1d |ai-hatai|2 = O(ε). In contrast, the strongest known prior results only yield a εO(1) (relative) bound on the distance between individual ai's and their estimates and translate into an O(dε) bound on the total variation distance. Our key innovation is a new approach to ICA (even to outlier-free ICA) that circumvents the difficulties in the classical method of moments and instead relies on a new geometric certificate of correctness of an affine transformation. Our algorithm is based on a new method that iteratively improves an estimate of the unknown affine transformation whenever the requirements of the certificate are not met.