Daniel Klötzl

Daniel Klötzl, Tim Krake, Frank Heyen et al.

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.

Daniel Klötzl, Ozan Tastekin, David Hägele et al.

Multidimensional data is often associated with uncertainties that are not well-described by normal distributions. In this work, we describe how such distributions can be projected to a low-dimensional space using uncertainty-aware principal component analysis (UAPCA). We propose to model multidimensional distributions using Gaussian mixture models (GMMs) and derive the projection from a general formulation that allows projecting arbitrary probability density functions. The low-dimensional projections of the densities exhibit more details about the distributions and represent them more faithfully compared to UAPCA mappings. Further, we support including user-defined weights between the different distributions, which allows for varying the importance of the multidimensional distributions. We evaluate our approach by comparing the distributions in low-dimensional space obtained by our method and UAPCA to those obtained by sample-based projections.