Non-Gaussian Component Analysis with Log-Density Gradient Estimation
Machine Learning
2016-01-29 v1
Abstract
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.
Cite
@article{arxiv.1601.07665,
title = {Non-Gaussian Component Analysis with Log-Density Gradient Estimation},
author = {Hiroaki Sasaki and Gang Niu and Masashi Sugiyama},
journal= {arXiv preprint arXiv:1601.07665},
year = {2016}
}