  Independent Component Analysis (ICA) is the problem of learning a square
matrix $A$, given samples of $X=AS$, where $S$ is a random vector with
independent coordinates. Most existing algorithms are provably efficient only
when each $S_i$ has finite and moderately valued fourth moment. However, there
are practical applications where this assumption need not be true, such as
speech and finance. Algorithms have been proposed for heavy-tailed ICA, but
they are not practical, using random walks and the full power of the ellipsoid
algorithm multiple times. The main contributions of this paper are:
  (1) A practical algorithm for heavy-tailed ICA that we call HTICA. We provide
theoretical guarantees and show that it outperforms other algorithms in some
heavy-tailed regimes, both on real and synthetic data. Like the current
state-of-the-art, the new algorithm is based on the centroid body (a first
moment analogue of the covariance matrix). Unlike the state-of-the-art, our
algorithm is practically efficient. To achieve this, we use explicit analytic
representations of the centroid body, which bypasses the use of the ellipsoid
method and random walks.
  (2) We study how heavy tails affect different ICA algorithms, including
HTICA. Somewhat surprisingly, we show that some algorithms that use the
covariance matrix or higher moments can successfully solve a range of ICA
instances with infinite second moment. We study this theoretically and
experimentally, with both synthetic and real-world heavy-tailed data.
