Parallel Coordinate Plots for Neighbor Retrieval

Jaakko Peltonen, Ziyuan Lin

2017

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.

References

  1. Achtert, E., Kriegel, H.-P., Schubert, E., and Zimek, A. (2013). Interactive data mining with 3d-parallelcoordinate-trees. In SIGMOD, pages 1009-1012, New York, NY, USA. ACM.
  2. Ankerst, M., Berchtold, S., and Keim, D. A. (1998). Similarity clustering of dimensions for an enhanced visualization of multidimensional data. In INFOVIS, pages 52-60.
  3. Caldas, J., Gehlenborg, N., Faisal, A., Brazma, A., and Kaski, S. (2009). Probabilistic retrieval and visualization of biologically relevant microarray experiments. Bioinformatics, 25:i145-i153.
  4. Claessen, J. and van Wijk, J. (2011). Flexible Linked Axes for Multivariate Data Visualization. IEEE T. Vis. Comput. Gr., 17:2310-2316.
  5. Fanea, E., Carpendale, S., and Isenberg, T. (2005). An interactive 3d integration of parallel coordinates and star glyphs. In INFOVIS, pages 149-156. IEEE.
  6. Fua, Y.-H., Ward, M. O., and Rundensteiner, E. A. (1999). Hierarchical parallel coordinates for exploration of large datasets. In VIS, pages 43-50. IEEE Computer Society Press.
  7. Guo, D. (2003). Coordinating computational and visual approaches for interactive feature selection and multivariate clustering. Inform. Vis., 2:232-246.
  8. Heinrich, J., Stasko, J., and Weiskoph, D. (2012). The parallel coordinates matrix. In Eurovis, pages 37-41.
  9. Heinrich, J. and Weiskopf, D. (2013). State of the Art of Parallel Coordinates. In EG2013 - STARs. The Eurographics Association.
  10. Herman, I., , Melanc¸on, G., and Marshall, M. S. (2000). Graph visualization and navigation in information visualization: a survey. IEEE T. Vis. Comput. Gr., 6:24- 43.
  11. Hinton, G. E. and Roweis, S. T. (2002). Stochastic neighbor embedding. In NIPS, pages 833-840.
  12. Inselberg, A. (2009). Parallel Coordinates: VISUAL Multidimensional Geometry and its Applications. Springer.
  13. Johansson, J., Ljung, P., Jern, M., and Cooper, M. (2006). Revealing structure in visualizations of dense 2d and 3d parallel coordinates. Inform. Vis., 5:125-136.
  14. Koropatkin, N. M., Cameron, E. A., and Martens, E. C. (2012). How glycan metabolism shapes the human gut microbiota. Nat. Rev. Microbiol., 10:323-335.
  15. Laplante, M. and Sabatini, D. M. (2012). mTOR signaling in growth control and disease. Cell, 149:274-293.
  16. Lichman, M. (2013). UCI machine learning repository.
  17. Makwana, H., Tanwani, S., and Jain, S. (2012). Axes reordering in parallel coordinate for pattern optimization. Int. J. Comput. Appl., 40:43-48.
  18. Parkinson, H. E. et al. (2009). Arrayexpress update - from an archive of functional genomics experiments to the atlas of gene expression. Nucleic Acids Res., 37:868- 872.
  19. Peltonen, J. and Kaski, S. (2011). Generative modeling for maximizing precision and recall in information visualization. In AISTATS 2011, volume 15, pages 597-587. JMLR W&CP.
  20. Peltonen, J. and Lin, Z. (2015). Information retrieval approach to meta-visualization. Mach. Learn., 99:189- 229.
  21. Peng, W., Ward, M., and Rundensteiner, E. (2004). Clutter Reduction in Multi-Dimensional Data Visualization Using Dimension Reordering. In INFOVIS, pages 89-96.
  22. Rapaport, E., Remy, P., Kleinkauf, H., Vater, J., and Zamecnik, P. C. (1987). Aminoacyl-tRNA synthetases catalyze AMP--ADP--ATP exchange reactions, indicating labile covalent enzyme-amino-acid intermediates. P. Natl. Acad. Sci. USA, 84:7891-7895.
  23. Rubinfeld, H. and Seger, R. (2005). The ERK cascade. Mol. Biotechnol., 31:151-174.
  24. Salvant, B. S., Fortunato, E. A., and Spector, D. H. (1998). Cell cycle dysregulation by human cytomegalovirus: influence of the cell cycle phase at the time of infection and effects on cyclin transcription. J. Virol., 72:3729-3741.
  25. Sanchez-Diaz, P. C. et al. (2013). De-regulated micrornas in pediatric cancer stem cells target pathways involved in cell proliferation, cell cycle and development. PLoS ONE, 8:1-10.
  26. Schloerke, B. et al. (2014). GGally: Extension to ggplot2. R package version 0.5.0.
  27. Shneiderman, B. (1996). The eyes have it: A task by data type taxonomy for information visualizations. In IEEE Symp. on Visual Languages, pages 336-343. IEEE Computer Society Press.
  28. Venna, J., Peltonen, J., Nybo, K., Aidos, H., and Kaski, S. (2010). Information retrieval perspective to nonlinear dimensionality reduction for data visualization. J. Mach. Learn. Res., 11:451-490.
  29. Viau, C. and McGuffin, M. J. (2012). ConnectedCharts: Explicit visualization of relationships between data graphics. Comput. Graph. Forum, 31:1285-1294.
  30. Vucenik, I. and Shamsuddin, A. M. (2006). Protection against cancer by dietary IP6 and inositol. Nutr. Cancer, 55:109-125.
  31. Warren, H. S. and Smyth, M. J. (1999). Nk cells and apoptosis. Immunol Cell Biol, 77:64-75.
  32. Wegenkittl, R., Löffelmann, H., and Gr öller, E. (1997). Visualizing the behaviour of higher dimensional dynamical systems. In VIS, pages 119-125. IEEE.
  33. Wismüller, A., Verleysen, M., Aupetit, M., and Lee, J. A. (2010). Recent advances in nonlinear dimensionality reduction, manifold and topological learning. In ESANN. d-side.
  34. Yang, Z., Peltonen, J., and Kaski, S. (2013). Scalable optimization of neighbor embedding for visualization. In ICML, pages 127-135.
  35. Zhao, Y. et al. (2010). Angiotensin II/angiotensin II type I receptor (AT1R) signaling promotes MCF-7 breast cancer cells survival via PI3-kinase/Akt pathway. J. Cell. Physiol., 225:168-173.
Download


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

@conference{ivapp17,
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)},
year={2017},
pages={40-51},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006097400400051},
isbn={978-989-758-228-8},
}


in EndNote Style

TY - CONF
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