The slowness principle: SFA can detect different slow components in non-stationary time series. (1st January 2011)

• Record Type: Journal Article
• Authors: Konen, Wolfgang; Koch, Patrick
• Is Part Of: International journal of innovative computing and applications. Volume 3:Number 1(2011)
• Page Start: 3
• Page End: 10
• Publication Date: 2011-01-01
• Subjects: driving force -- driving force detection -- human perception -- logistic map -- non-linear regression -- non-stationary time series -- phase transition -- slow feature analysis -- SFA -- slowness principle -- unsupervised learning; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• DOI: 10.1504/IJICA.2011.037946
• Languages: English
• ISSNs: 1751-648X

Abstract

Slow feature analysis (SFA) is a bioinspired method for extracting slowly varying driving forces from quickly varying non-stationary time series. We show here that it is possible for SFA to detect a component which is even slower than the driving force itself (e.g., the envelope of a modulated sine wave). It depends on circumstances like the embedding dimension, the time series predictability, or the base frequency, whether the driving force itself or a slower subcomponent is detected. Interestingly, we observe a swift phase transition from one regime to another and it is the objective of this work to quantify the influence of various parameters on this phase transition. We conclude that what is perceived as slow by SFA varies and that a more or less fast switching from one regime to another occurs, perhaps showing some similarity to human perception.