NMF-Based Analysis of Mobile Eye-Tracking Data
Daniel Klötzl Tim Krake Frank Heyen Michael Becher Maurice Koch Daniel Weiskopf Kuno Kurzhals
The depiction of scanpaths from mobile eye-tracking recordings by thumbnails
from the stimulus allows the application of visual computing to detect areas of
interest in an unsupervised way. We suggest using nonnegative matrix
factorization (NMF) to identify such areas in stimuli. For a user-defined
integer k, NMF produces an explainable decomposition into k components, each
consisting of a spatial representation associated with a temporal indicator. In
the context of multiple eye-tracking recordings, this leads to k spatial
representations, where the temporal indicator highlights the appearance within
recordings. The choice of k provides an opportunity to control the refinement
of the decomposition, i.e., the number of areas to detect. We combine our
NMF-based approach with visualization techniques to enable an exploratory
analysis of multiple recordings. Finally, we demonstrate the usefulness of our
approach with mobile eye-tracking data of an art gallery.