Parallel Coordinate Plots for Neighbor Retrieval

Jaakko Peltonen, Ziyuan Lin


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.


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Paper Citation

in Harvard Style

Peltonen J. and Lin Z. (2017). Parallel Coordinate Plots for Neighbor Retrieval . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP, (VISIGRAPP 2017) ISBN 978-989-758-228-8, pages 40-51. DOI: 10.5220/0006097400400051

in Bibtex Style

author={Jaakko Peltonen and Ziyuan Lin},
title={Parallel Coordinate Plots for Neighbor Retrieval},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP, (VISIGRAPP 2017)},

in EndNote Style

JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP, (VISIGRAPP 2017)
TI - Parallel Coordinate Plots for Neighbor Retrieval
SN - 978-989-758-228-8
AU - Peltonen J.
AU - Lin Z.
PY - 2017
SP - 40
EP - 51
DO - 10.5220/0006097400400051