Non-Gaussian Component Analysis with Log-Density Gradient Estimation

Non-Gaussian component analysis (NGCA) is aimed at identifying a linear subspace such that the projected data follows a nonGaussian distribution. In this paper, we propose a novel NGCA algorithm based on logdensity 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.