  Extracting coherent patterns is one of the standard approaches towards
understanding spatio-temporal data. Dynamic mode decomposition (DMD) is a
powerful tool for extracting coherent patterns, but the original DMD and most
of its variants do not consider label information, which is often available as
side information of spatio-temporal data. In this work, we propose a new method
for extracting distinctive coherent patterns from labeled spatio-temporal data
collections, such that they contribute to major differences in a labeled set of
dynamics. We achieve such pattern extraction by incorporating discriminant
analysis into DMD. To this end, we define a kernel function on subspaces
spanned by sets of dynamic modes and develop an objective to take both
reconstruction goodness as DMD and class-separation goodness as discriminant
analysis into account. We illustrate our method using a synthetic dataset and
several real-world datasets. The proposed method can be a useful tool for
exploratory data analysis for understanding spatio-temporal data.
