Using Slisemap to interpret physical data
Lauri Seppäläinen Anton Björklund Vitus Besel Kai Puolamäki
Manifold visualisation techniques are commonly used to visualise
high-dimensional datasets in physical sciences. In this paper we apply a
recently introduced manifold visualisation method, called Slise, on datasets
from physics and chemistry. Slisemap combines manifold visualisation with
explainable artificial intelligence. Explainable artificial intelligence is
used to investigate the decision processes of black box machine learning models
and complex simulators. With Slisemap we find an embedding such that data items
with similar local explanations are grouped together. Hence, Slisemap gives us
an overview of the different behaviours of a black box model. This makes
Slisemap into a supervised manifold visualisation method, where the patterns in
the embedding reflect a target property. In this paper we show how Slisemap can
be used and evaluated on physical data and that Slisemap is helpful in finding
meaningful information on classification and regression models trained on these
datasets.