Methods Promoting Usability in Topological Data Analysis
Topological data analysis (TDA) is a class of machine learning models that exploits the
underlying structure or topology of data. Extensive theoretical work in this space
has led to open source (Mapper) and commercial (Ayasdi) tools that enable
TDA for practical data analytics, but users must still work in a
complicated space of parameters and mapping functions.
This project builds new methods that promote
the usability of TDA my making this search space easier to
navigate. It also investigates extensions of TDA
for (semi)-supervised learning, data imputation, and
feature representation tasks.
Students: Kyle Brown, Matt Piekenbrock
The This work is supported by the Oak Ridge Institute for Science and
Education and the Air Force Research Laboratories.