  Non-Gaussian component analysis (NGCA) is aimed at identifying a linear
subspace such that the projected data follows a non-Gaussian distribution. In
this paper, we propose a novel NGCA algorithm based on log-density gradient
estimation. Unlike existing methods, the proposed NGCA algorithm identifies the
linear subspace by using the eigenvalue decomposition without any iterative
procedures, and thus is computationally reasonable. Furthermore, through
theoretical analysis, we prove that the identified subspace converges to the
true subspace at the optimal parametric rate. Finally, the practical
performance of the proposed algorithm is demonstrated on both artificial and
benchmark datasets.
