Exploring High-Dimensional Structure via Axis-Aligned Decomposition of Linear Projections
| dc.contributor.author | Thiagarajan, Jayaraman J. | en_US |
| dc.contributor.author | Liu, Shusen | en_US |
| dc.contributor.author | Ramamurthy, Karthikeyan Natesan | en_US |
| dc.contributor.author | Bremer, Peer-Timo | en_US |
| dc.contributor.editor | Jeffrey Heer and Heike Leitte and Timo Ropinski | en_US |
| dc.date.accessioned | 2018-06-02T18:08:01Z | |
| dc.date.available | 2018-06-02T18:08:01Z | |
| dc.date.issued | 2018 | |
| dc.description.abstract | Two-dimensional embeddings remain the dominant approach to visualize high dimensional data. The choice of embeddings ranges from highly non-linear ones, which can capture complex relationships but are difficult to interpret quantitatively, to axis-aligned projections, which are easy to interpret but are limited to bivariate relationships. Linear project can be considered as a compromise between complexity and interpretability, as they allow explicit axes labels, yet provide significantly more degrees of freedom compared to axis-aligned projections. Nevertheless, interpreting the axes directions, which are often linear combinations of many non-trivial components, remains difficult. To address this problem we introduce a structure aware decomposition of (multiple) linear projections into sparse sets of axis-aligned projections, which jointly capture all information of the original linear ones. In particular, we use tools from Dempster-Shafer theory to formally define how relevant a given axis-aligned project is to explain the neighborhood relations displayed in some linear projection. Furthermore, we introduce a new approach to discover a diverse set of high quality linear projections and show that in practice the information of k linear projections is often jointly encoded in ~ k axis-aligned plots. We have integrated these ideas into an interactive visualization system that allows users to jointly browse both linear projections and their axis-aligned representatives. Using a number of case studies we show how the resulting plots lead to more intuitive visualizations and new insights. | en_US |
| dc.description.number | 3 | |
| dc.description.sectionheaders | Structure and Shape | |
| dc.description.seriesinformation | Computer Graphics Forum | |
| dc.description.volume | 37 | |
| dc.identifier.doi | 10.1111/cgf.13416 | |
| dc.identifier.issn | 1467-8659 | |
| dc.identifier.pages | 241-251 | |
| dc.identifier.uri | https://doi.org/10.1111/cgf.13416 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.1111/cgf13416 | |
| dc.publisher | The Eurographics Association and John Wiley & Sons Ltd. | en_US |
| dc.title | Exploring High-Dimensional Structure via Axis-Aligned Decomposition of Linear Projections | en_US |