Abstract
Parallel Coordinate Plots (PCPs) are a prominent approach to visualize the full feature set of high-dimensional vectorial data, either standalone or complementing other visualizations like scatter plots. Optimization of PCPs has concentrated on ordering and positioning of the coordinate axes based on various statistical criteria. We introduce a new method to construct PCPs that are directly optimized to support a common data analysis task: analyzing neighborhood relationships of data items within each coordinate axis and across the axes. We optimize PCPs on 1D lines or 2D planes for accurate viewing of neighborhood relationships among data items, measured as an information retrieval task. Both the similarity measurement between axes and the axis positions are directly optimized for accurate neighbor retrieval. The resulting method, called Parallel Coordinate Plots for Neighbor Retrieval (PCP-NR), achieves better information retrieval performance than traditional PCPs in experiments.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications |
| Publisher | SciTePress |
| Pages | 40-51 |
| Number of pages | 12 |
| Volume | 3 |
| ISBN (Electronic) | 9789897582288 |
| Publication status | Published - 1 Jan 2017 |
| MoE publication type | A4 Conference publication |
| Event | International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Porto, Portugal Duration: 27 Feb 2017 → 1 Mar 2017 Conference number: 12 |
Conference
| Conference | International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications |
|---|---|
| Abbreviated title | VISIGRAPP |
| Country/Territory | Portugal |
| City | Porto |
| Period | 27/02/2017 → 01/03/2017 |
Keywords
- Dimensionality reduction
- Machine learning
- Parallel coordinates
- Visualization