Non-Gaussian Component Analysis with Log-Density Gradient Estimation
This addresses a specific problem in statistical analysis for researchers, but appears incremental as it builds on existing NGCA methods with computational improvements.
The paper tackles the problem of identifying a linear subspace where projected data follows a non-Gaussian distribution by proposing a novel NGCA algorithm based on log-density gradient estimation, achieving convergence to the true subspace at the optimal parametric rate.
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.