Heuristic Evaluation of Expansions for Non-linear Hierarchical Slow Feature Analysis

Slow Feature Analysis (SFA) is a feature extraction algorithm based on the slowness principle with applications to both supervised and unsupervised learning. When implemented hierarchically, it allows for efficient processing of high-dimensional data, such as images. Expansion plays a crucial role in the implementation of non-linear SFA. In this paper, a fast heuristic method for the evaluation of expansions is proposed, consisting of tests on seven problems and two metrics. Several expansions with different complexities are evaluated. It is shown that the method allows predictions of the performance of SFA on a concrete data set, and the use of normalized expansions is justified. The proposed method is useful for the design of powerful expansions that allow the extraction of complex high-level features and provide better generalization.