A study of the fixed points and spurious solutions of the FastICA algorithm

TL;DR

This paper investigates the fixed points and spurious solutions of the FastICA algorithm, revealing conditions under which non-true solutions occur and providing guidance on nonlinearity choices for reliable results.

Contribution

It characterizes the relationship between demixing vectors, fixed points, and local optimizers, and analyzes scenarios leading to spurious solutions, especially with bimodal Gaussian mixtures.

Findings

Spurious solutions can be attractive fixed points under certain distributions.

Abstract

The FastICA algorithm is one of the most popular iterative algorithms in the domain of linear independent component analysis. Despite its success, it is observed that FastICA occasionally yields outcomes that do not correspond to any true solutions (known as demixing vectors) of the ICA problem. These outcomes are commonly referred to as spurious solutions. Although FastICA is among the most extensively studied ICA algorithms, the occurrence of spurious solutions are not yet completely understood by the community. In this contribution, we aim at addressing this issue. In the first part of this work, we are interested in the relationship between demixing vectors, local optimizers of the contrast function and (attractive or unattractive) fixed points of FastICA algorithm. Characterizations of these sets are given, and an inclusion relationship is discovered. In the second part, we investigate the possible scenarios where spurious solutions occur. We show that when certain bimodal Gaussian mixtures distributions are involved, there may exist spurious solutions that are attractive fixed points of FastICA. In this case, popular nonlinearities such as "gauss" or "tanh" tend to yield spurious solutions, whereas only "kurtosis" may give reliable results. Some advices are given for the practical choice of nonlinearity function.