2.3 Slow feature analysis

Slow feature analysis [20] is an unsupervised learning method, whereby functions g xð Þ are identified to extract slowly varying features yð Þt from rapidly varying signals xð Þt . This is done virtually instantaneously, that is, one time slice of the output is based on very few time slices of the input. Extensions of the method have been proposed by other authors [21–23].
