Stable Visual Summaries for Trajectory Collections
Jules Wulms, Juri Buchm\"uller, Wouter Meulemans, Kevin Verbeek,, Bettina Speckmann

TL;DR
This paper introduces a new stable dimensionality reduction method called Stable Principal Component (SPC) for visual summaries of trajectory collections, balancing spatial quality and stability efficiently.
Contribution
The paper presents the SPC method, explicitly parameterized for stability, and demonstrates its superior stability and efficiency compared to existing approaches.
Findings
SPC outperforms other methods in stability metrics.
SPC maintains spatial quality comparable to state-of-the-art methods.
SPC is computationally more efficient than existing stable dimensionality reduction techniques.
Abstract
The availability of devices that track moving objects has led to an explosive growth in trajectory data. When exploring the resulting large trajectory collections, visual summaries are a useful tool to identify time intervals of interest. A typical approach is to represent the spatial positions of the tracked objects at each time step via a one-dimensional ordering; visualizations of such orderings can then be placed in temporal order along a time line. There are two main criteria to assess the quality of the resulting visual summary: spatial quality -- how well does the ordering capture the structure of the data at each time step, and stability -- how coherent are the orderings over consecutive time steps or temporal ranges? In this paper we introduce a new Stable Principal Component (SPC) method to compute such orderings, which is explicitly parameterized for stability, allowing a…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsData Management and Algorithms · Data Visualization and Analytics · Time Series Analysis and Forecasting
