… In this paper, we presented a novel, probabilistic approach to Slow Feature Analysis.
Specifically, we extended SFA to a fully probabilistic EM model (EM-SFA), while we augmented …

… diagnosis method based on slow feature analysis has been … In this paper, a recursive slow
feature analysis algorithm for … An important algebraic property of slow feature analysis is first …

… introduction of slow feature analysis (SFA), mainly focusing on why the learned ’slow’ features
are effective in human motion analysis and how we use SFA to extract these features from …

… For the purposes of this study, it is sufficient to remember that slow feature analysis finds
input-output functions that extract slowly varying features from a typical input signal in a …

… Slow Feature Analysis (SFA) is an unsupervised learning algorithm which extracts slowly
varying features from … , we enable parameterized slow feature extraction through a preset filter. …

… -level visual signals change slowly over time, it … slow feature analysis to “steady” feature
analysis. The key idea is to impose a prior that higher order derivatives in the learned feature …

Slow Feature Analysis (SFA) is an unsupervised learning algorithm based on the slowness
principle and has originally been developed to learn invariances in a model of the primate …

… A new process monitoring strategy based on slow feature analysis (SFA) is proposed for the
… Slow features as LVs are developed to describe slowly varying dynamics, yielding improved …

… its brilliant performance in many fields, including feature extraction and projection.
Therefore, in this paper, based on the deep network and slow feature analysis (SFA) theory, we …

… To this end, a novel process monitoring scheme based on slow feature analysis (… slowly
varying components that underlie multi-dimensional time series data, termed as slow features…