Evaluating Slow Feature Analysis on Time-Series Data

Abstract

In this thesis, we investigate Slow Feature Analysis (SFA) as a method of extracting slowly-varying signals from quickly-varying input data. The main aim of the thesis is two-fold. The first primary objective is to evaluate how the level of noise in input data affects the performance of SFA for different input feature combinations. The second objective of this thesis is to compare the performance of the classical formulation of SFA to a biologically plausible version of the algorithm. The first half of the thesis gives reader a theoretical overview of how the algorithm works and explores some of the previous applications. The second half conducts three experiments that explore the primary research questions of the thesis and discusses possible further research directions.