Parallel coordinate plots for neighbor retrieval

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

5 Citations (Scopus)

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 languageEnglish
Title of host publicationProceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
Pages40-51
Number of pages12
Volume3
ISBN (Electronic)9789897582288
Publication statusPublished - 1 Jan 2017
MoE publication typeA4 Article in a conference publication
EventInternational Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Porto, Portugal
Duration: 27 Feb 20171 Mar 2017
Conference number: 12

Conference

ConferenceInternational Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
Abbreviated titleVISIGRAPP
CountryPortugal
CityPorto
Period27/02/201701/03/2017

Keywords

  • Dimensionality reduction
  • Machine learning
  • Parallel coordinates
  • Visualization

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