On the Estimation of Entropy in the FastICA Algorithm
This highlights a cautionary issue for researchers and practitioners using fastICA for dimension reduction, indicating it is incremental as it critiques an existing method.
The paper identifies that approximations in the fastICA algorithm can cause it to fail to recognize clear patterns in data, as demonstrated with a two-dimensional example where it misses visible structure.
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.