… features of temporally varying signals are useful for analysis and classification. Slow
feature analysis (SFA) is a new method for learning invariant or slowly varying features from a …

… A slowly varying feature is considered to represent more … to vary as slowly as possible,
so they are called slow features. … trend by searching “slow feature analysis” through Google …

Figure 1: Schematics of the optimization problem solved by Slow Feature Analysis. Given a
set of time-varying input signals, x (t), SFA learns instantaneous, non-linear functions g (x) …

… In this paper, we presented four slow feature analysis-based methods for recognizing
human actions. The original unsupervised SFA algorithm is extended with different learning …

… slow feature analysis (SFA), an unsupervised dimension reduction methodology [16]. Slow
features s … operations, described by time differences of slow features s . In this way, abnormal …

… This perspective inspired a number of variants on the traditional slow feature analysis
algorithm. First, a fully probabilistic version of slow features analysis was shown to be capable of …

… Slow feature analysis (SFA) is a new unsupervised algorithm to learn nonlinear functions
that extract slowly … In this context in order to be slowly varying the functions learned by SFA …

… a novel slow feature analysis (SFA) algorithm for change detection. Compared with changed
pixels, the unchanged ones should be spectrally invariant and varying slowly across the …

… of image scenes are much more complicated in a high-dimensional feature space. Therefore,
in this paper, we propose kernel slow feature analysis (KSFA) with supervised training …

The Slow Feature Analysis (SFA) unsupervised learning framework extracts features
representing the underlying causes of the changes within a temporally coherent high-dimensional …