2003/12/12 by Laurenz Wiskott, Wiskott, Laurenz
Engineering · Neuroscience · Physics and Astronomy · #Advanced Chemical Sensor Technologies #Chaos control and synchronization #FOS: Physical sciences #Neural dynamics and brain function #Statistical Mechanics (cond-mat.stat-mech) #cond-mat.stat-mech
paper · pdf · doi:10.48550/arxiv.cond-mat/0312317
8 pages, 4 figures
arxiv created 2003/12/12 · openalex publication_date 2003/12/12 · arxiv updated 2009/12/01 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
Slow feature analysis (SFA) is a new technique for extracting slowly varying features from a quickly varying signal. It is shown here that SFA can be applied to nonstationary time series to estimate a single underlying driving force with high accuracy up to a constant offset and a factor. Examples with a tent map and a logistic map illustrate the performance.