2009/11/23 by Wolfgang Konen, Konen, Wolfgang, Patrick Koch +1
Neuroscience · Physics and Astronomy · #Chaos control and synchronization #FOS: Computer and information sciences #Machine Learning (stat.ML) #Mechanical and Optical Resonators #Neural dynamics and brain function
paper · pdf · doi:10.48550/arxiv.0911.4397
openalex publication_date 2009/11/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Slow feature analysis (SFA) is a method for extracting slowly varying driving forces from quickly varying nonstationary 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 is shown that 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. We observe a phase transition from one regime to the other and it is the purpose of this work to quantify the influence of various parameters on this phase transition. We conclude that what is percieved as slow by SFA varies and that a more or less fast switching from one regime to the other occurs, perhaps showing some similarity to human perception.