On the Estimation of Entropy in the FastICA Algorithm
Machine Learning
2020-09-09 v5 Machine Learning
Computation
Abstract
The fastICA method is a popular dimension reduction technique used to reveal patterns in data. Here we show both theoretically and in practice that the approximations used in fastICA can result in patterns not being successfully recognised. We demonstrate this problem using a two-dimensional example where a clear structure is immediately visible to the naked eye, but where the projection chosen by fastICA fails to reveal this structure. This implies that care is needed when applying fastICA. We discuss how the problem arises and how it is intrinsically connected to the approximations that form the basis of the computational efficiency of fastICA.
Keywords
Cite
@article{arxiv.1805.10206,
title = {On the Estimation of Entropy in the FastICA Algorithm},
author = {Elena Issoglio and Paul Smith and Jochen Voss},
journal= {arXiv preprint arXiv:1805.10206},
year = {2020}
}
Comments
22 pages, 4 figures