… between linear slow feature analysis and second-order independent component analysis,
and … case of several time delays and discuss two possible extensions of slow feature analysis. …

… This paper proposes integrating slow feature analysis (SFA) … Then the dominant slow
features are selected as the inputs of … of slowly varying latent variables, known as slow features. …

… on dynamic slow feature analysis (DSFA) and random forest (RF) is proposed in this paper.
First, the augmented matrix technique is embedded into the slow feature analysis model to …

… We propose an extension of slow feature analysis (SFA) for supervised dimensionality
reduction called graph-based SFA (GSFA). The algorithm extracts a label-predictive low-…

… Simulations are based on a hierarchical Slow Feature Analysis (SFA) network topped by a
principal component analysis (ICA) output layer. The slowness principle is shown to account …

… We use Slow Feature Analysis (SFA) which leads to improved … the slow features a low order
feature set is formed which is used together with color and Walsh Hadamard texture features …

… Therefore, this article develops an interval-aware probabilistic slow feature analysis (IA-PSFA)
method along with the corresponding monitoring strategy to address the above problems …

… radiometric normalization method with iterative slow feature analysis (ISFA) to reduce the
radiometric variance. Slow feature analysis extracts invariant features from the quickly varying …

… dynamic data analysis tool, kernel slow feature analysis (… multiway global preserving kernel
slow feature analysis (MGKSFA), … preserving-based kernel slow feature analysis (GKSFA) is …

… To optimize the slowness-based objective function, we use the slow feature analysis (SFA)
algorithm (see section 2.2). Except for minor changes, the model used here is identical to that …