# NMF-Based Analysis of Mobile Eye-Tracking Data

arXiv: https://arxiv.org/abs/2404.03417 · HTML: https://mlsanity.com/paper/2404.03417/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 30 references that are in the map.
- First posted on arXiv 4 Apr 2024.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Deep semantic gaze embedding and scanpath comparison for expertise classification during OPT viewing](https://mlsanity.com/paper/2003.13987/) (2020)
- [A High-Level Description and Performance Evaluation of Pupil Invisible](https://mlsanity.com/paper/2009.00508/) (2020)
- [Task Classification Model for Visual Fixation, Exploration, and Search](https://mlsanity.com/paper/1907.12635/) (2019)
- [Scalability in Visualization](https://mlsanity.com/paper/2210.06562/) (2022)
- [A Spiral into the Mind: Gaze Spiral Visualization for Mobile Eye Tracking](https://mlsanity.com/paper/2204.13494/) (2022)

## Abstract (arXiv)

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.