Non-Gaussian Component Analysis via Lattice Basis Reduction
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Non-Gaussian Component Analysis (NGCA) is the following distribution learning problem: Given i.i.d. samples from a distribution on ℝ^d that is non-gaussian in a hidden direction v and an independent standard Gaussian in the orthogonal directions, the goal is to approximate the hidden direction v. Prior work <cit.> provided formal evidence for the existence of an information-computation tradeoff for NGCA under appropriate moment-matching conditions on the univariate non-gaussian distribution A. The latter result does not apply when the distribution A is discrete. A natural question is whether information-computation tradeoffs persist in this setting. In this paper, we answer this question in the negative by obtaining a sample and computationally efficient algorithm for NGCA in the regime that A is discrete or nearly discrete, in a well-defined technical sense. The key tool leveraged in our algorithm is the LLL method <cit.> for lattice basis reduction.
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