  We present a new technique called "DSNE" which learns the velocity embeddings
of low dimensional map points when given the high-dimensional data points with
its velocities. The technique is a variation of Stochastic Neighbor Embedding,
which uses the Euclidean distance on the unit sphere between the unit-length
velocity of the point and the unit-length direction from the point to its near
neighbors to define similarities, and try to match the two kinds of
similarities in the high dimension space and low dimension space to find the
velocity embeddings on the low dimension space. DSNE can help to visualize how
the data points move in the high dimension space by presenting the movements in
two or three dimensions space. It is helpful for understanding the mechanism of
cell differentiation and embryo development.
