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Information retrieval approach to meta-visualization

Research output: Contribution to journalArticleScientificpeer-review

Details

Original languageEnglish
Pages (from-to)189-229
Number of pages41
JournalMachine Learning
Volume99
Issue number2
DOIs
Publication statusPublished - 1 May 2015
Publication typeA1 Journal article-refereed

Abstract

Visualization is crucial in the first steps of data analysis. 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. Visualization has recently been formalized as an information retrieval task; we extend this approach, and formalize meta-visualization as an information retrieval task whose performance can be rigorously quantified and optimized. 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. In experiments we show such meta-visualization outperforms alternatives, and yields insight into data in several case studies.

ASJC Scopus subject areas

Keywords

  • Meta-visualization, Neighbor embedding, Nonlinear dimensionality reduction