Information retrieval perspective to meta-visualization
Research output: Contribution to journal › Article › Scientific › peer-review
Details
| Original language | English |
|---|---|
| Pages (from-to) | 165-180 |
| Number of pages | 16 |
| Journal | Journal of Machine Learning Research |
| Volume | 29 |
| Publication status | Published - 2013 |
| Publication type | A1 Journal article-refereed |
Abstract
In visual data exploration with scatter plots, no single plot is sufficient to analyze complicated high-dimensional data sets. Given numerous visualizations created with different features or methods, meta-visualization is needed to analyze the visualizations together. We solve how to arrange numerous visualizations onto a meta-visualization display, so that their similarities and differences can be analyzed. We introduce a machine learning approach to optimize the meta-visualization, based on an information retrieval perspective: Two visualizations are similar if the analyst would retrieve similar neighborhoods between data samples from either visualization. Based on the approach, we introduce a nonlinear embedding method for meta-visualization: it optimizes locations of visualizations on a display, so that visualizations giving similar information about data are close to each other.
ASJC Scopus subject areas
Keywords
- Meta-visualization, Neighbor embedding, Nonlinear dimensionality reduction