  We propose graph-based predictable feature analysis (GPFA), a new method for
unsupervised learning of predictable features from high-dimensional time
series, where high predictability is understood very generically as low
variance in the distribution of the next data point given the previous ones. We
show how this measure of predictability can be understood in terms of graph
embedding as well as how it relates to the information-theoretic measure of
predictive information in special cases. We confirm the effectiveness of GPFA
on different datasets, comparing it to three existing algorithms with similar
objectives---namely slow feature analysis, forecastable component analysis, and
predictable feature analysis---to which GPFA shows very competitive results.
