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Information retrieval perspective to meta-visualization

Research output: Contribution to journalArticleScientificpeer-review

Details

Original languageEnglish
Pages (from-to)165-180
Number of pages16
JournalJournal of Machine Learning Research
Volume29
Publication statusPublished - 2013
Publication typeA1 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.

Keywords

  • Meta-visualization, Neighbor embedding, Nonlinear dimensionality reduction