Speaker: Darrick Lee, University of Pennsylvania
Time: September 30, 2020
Abstract: Path signatures are powerful nonparametric tools for time
series analysis, shown to form a universal and characteristic feature
map for Euclidean valued time series data. The theory of path
signatures can be lifted to the setting of Lie group valued time
series while retaining their universal and characteristic properties.
This talk will introduce these generalized path signatures on Lie
groups and discuss their topological origins in Chen’s cochain model
for path spaces. We will then consider these signatures in the context
of kernel methods in machine learning, and demonstrate an application
to the human action recognition problem in computer vision.
Recording: available here with access password: DLQ+#W3$