Slow feature analysis is motivated by a general principle called the slowness principle. The idea is that the important characteristics of scenes change very slowly compared to the individual measurements that make up a description of a scene. The slowness principal may be introduced by adding a term to the cost function measuring the difference between feature extractors according to time. The SFA algorithm consists of defining $f(x; \theta)$ to be a linear transformation and solving the optimization problem: $min_{\theta}E_t (f(x^{t+1})_i-f(x^{t})_i)^2$ subject to the constraints: $E_t (f(x^{t})_i)=0$ and $E_t [f(x^{t})^2_i]=1$