Evaluating Slow Feature Analysis on Time-Series Data
Date
2021
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Tartu Ülikool
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.
Description
Keywords
Machine learning, unsupervised learning, Slow Feature Analysis, neural networks