On the numeric stability of the SFA implementation sfa-tk

Slow feature analysis (SFA) is an information processing method proposed by Wiskott and Sejnowski (WS02) which allows to extract slowly varying signals from complex multidimensional time series. Wiskott (Wis98) formulated a similar idea already before as a model of unsupervised learning of invariances in the visual system of vertebrates. SFA has been applied successfully to numerous different tasks: to reproduce a wide range of properties of complex cells in primary visual cortex (BW05), to model the self-organized formation of place cells in the hippocampus (FSW07), to classify handwritten digits (Ber05) and to extract driving forces from nonstationary time series (Wis03). The analysis of nonstationary time series plays an important role in the data understanding of various phenomena such as temperature drift in experimental setup, global warming in climate data or varying heart rate in cardiology. Such nonstationarities can be modeled by underlying parameters, referred to as driving forces, that change the dynamics of the system smoothly on a slow time scale or abruptly but rarely, e.g. if the dynamics switches between different discrete states.