  Recurrent neural networks (RNNs) in combination with a pooling operator and
the neighbourhood components analysis (NCA) objective function are able to
detect the characterizing dynamics of sequences and embed them into a
fixed-length vector space of arbitrary dimensionality. Subsequently, the
resulting features are meaningful and can be used for visualization or nearest
neighbour classification in linear time. This kind of metric learning for
sequential data enables the use of algorithms tailored towards fixed length
vector spaces such as R^n.
