  Signature is an infinite graded sequence of statistics known to characterize
geometric rough paths, which includes the paths with bounded variation. This
object has been studied successfully for machine learning with mostly
applications in low dimensional cases. In the high dimensional case, it suffers
from exponential growth in the number of features in truncated signature
transform. We propose a novel neural network based model which borrows the idea
from Convolutional Neural Network to address this problem. Our model reduces
the number of features efficiently in a data dependent way. Some empirical
experiments are provided to support our model.
