Data Visualization Using Interactive Dimensionality Reduction and Improved Color-Based Interaction Model

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10338)


This work presents an improved interactive data visualization interface based on a mixture of the outcomes of dimensionality reduction (DR) methods. Broadly, it works as follows: The user can input the mixture weighting factors through a visual and intuitive interface with a primary-light-colors-based model (Red, Green, and Blue). By design, such a mixture is a weighted sum of the color tone. Additionally, the low-dimensional representation space produced by DR methods are graphically depicted using scatter plots powered via an interactive data-driven visualization. To do so, pairwise similarities are calculated and employed to define the graph to simultaneously be drawn over the scatter plot. Our interface enables the user to interactively combine DR methods by the human perception of color, while providing information about the structure of original data. Then, it makes the selection of a DR scheme more intuitive -even for non-expert users.


Color-based model Data visualization Dimensionality reduction Pairwise similarity 



The authors would like to thank the project “Desarrollo de una metodología de visualización interactiva y eficaz de información en Big Data” supported by VIPRI from Universidad de Nariño - Colombia, as well as Universidad Técnica del Norte - Ecuador.


  1. 1.
    Ward, M.O., Grinstein, G., Keim, D.: Interactive Data Visualization: Foundations, Techniques, and Applications. CRC Press, Boca Raton (2010)zbMATHGoogle Scholar
  2. 2.
    Salazar-Castro, J., Rosas-Narváez, Y., Pantoja, A., Alvarado-Pérez, J.C., Peluffo-Ordóñez, D.H.: Interactive interface for efficient data visualization via a geometric approach. In: 2015 20th Symposium on Signal Processing, Images and Computer Vision (STSIVA), pp. 1–6. IEEE (2015)Google Scholar
  3. 3.
    Peña-Unigarro, D.F., Salazar-Castro, J.A., Peluffo-Ordóñez, D.H., Rosero-Montalvo, P.D., Oña-Rocha, O.R., Isaza, A.A., Alvarado-Pérez, J.C., Theron, R.: Interactive visualization methodology of high-dimensional data with a color-based model for dimensionality reduction. In: 2016 XXI Symposium on Signal Processing, Images and Artificial Vision (STSIVA), pp. 1–7, August 2016Google Scholar
  4. 4.
    Alvarado-Pérez, J.C., Peluffo-Ordóñez, D.H., Therón, R.: Visualización y métodos kernel: integrando inteligencia natural y artificial (2016)Google Scholar
  5. 5.
    Dai, W., Hu, P.: Research on personalized behaviors recommendation system based on cloud computing. Indones. J. Electr. Eng. Comput. Sci. 12(2), 1480–1486 (2013)Google Scholar
  6. 6.
    Dastan, M.: The role of visual perception in data visualization. J. Vis. Lang. Comput. 13(6), 601–622 (2002)CrossRefGoogle Scholar
  7. 7.
    Peluffo-Ordóñez, D.H., Alvarado-Pérez, J.C., Lee, J.A., Verleysen, M., et al.: Geometrical homotopy for data visualization. In: European Symposium on Artificial Neural Networks (ESANN 2015). Computational Intelligence and Machine Learning. (2015)Google Scholar
  8. 8.
    Díaz, I., Cuadrado, A.A., Pérez, D., García, F.J., Verleysen, M.: Interactive dimensionality reduction for visual analytics. In: Proceedings of the 22th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2014), pp. 183–188. Citeseer (2014)Google Scholar
  9. 9.
    Rosero-Montalvo, P., Diaz, P., Salazar-Castro, J.A., Peña-Unigarro, D.F., Anaya-Isaza, A.J., Alvarado-Pérez, J.C., Therón, R., Peluffo-Ordóñez, D.H.: Interactive data visualization using dimensionality reduction and similarity-based representations. In: Beltrán-Castañón, C., Nyström, I., Famili, F. (eds.) CIARP 2016. LNCS, vol. 10125, pp. 334–342. Springer, Cham (2017). doi: 10.1007/978-3-319-52277-7_41 CrossRefGoogle Scholar
  10. 10.
    Borg, I., Groenen, P.J.: Modern Multidimensional Scaling: Theory and Applications. Springer Science & Business Media, New York (2005)zbMATHGoogle Scholar
  11. 11.
    Peluffo-Ordóñez, D.H., Lee, J.A., Verleysen, M.: Short review of dimensionality reduction methods based on stochastic neighbour embedding. In: Villmann, T., Schleif, F.-M., Kaden, M., Lange, M. (eds.) Advances in Self-Organizing Maps and Learning Vector Quantization. AISC, vol. 295, pp. 65–74. Springer, Cham (2014). doi: 10.1007/978-3-319-07695-9_6 CrossRefGoogle Scholar
  12. 12.
    Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput. 15(6), 1373–1396 (2003)CrossRefzbMATHGoogle Scholar
  13. 13.
    Park, Y., Cafarella, M., Mozafari, B.: Visualization-aware sampling for very large databases. In: 2016 IEEE 32nd International Conference on Data Engineering (ICDE), pp. 755–766, May 2016Google Scholar
  14. 14.
    Emberson, L.L., Amso, D.: Learning to sample: eye tracking and fMRI indices of changes in object perception. J. Cogn. Neurosci. 24(10), 2030–2042 (2012)CrossRefGoogle Scholar
  15. 15.
    Bertini, E., Lalanne, D.: Surveying the complementary role of automatic data analysis and visualization in knowledge discovery. In: Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery: Integrating Automated Analysis with Interactive Exploration, pp. 12–20. ACM (2009)Google Scholar
  16. 16.
    Peluffo-Ordóñez, D.H., Lee, J.A., Verleysen, M.: Generalized kernel framework for unsupervised spectral methods of dimensionality reduction. In: 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), pp. 171–177. IEEE (2014)Google Scholar
  17. 17.
    Levkowitz, H.: Color Theory and Modeling for Computer Graphics, Visualization, and Multimedia Applications. Springer, New York (1997)CrossRefGoogle Scholar
  18. 18.
    Dix, A.: Human-Computer Interaction. Springer, New York (2009)Google Scholar
  19. 19.
    Lee, J.A., Renard, E., Bernard, G., Dupont, P., Verleysen, M.: Type 1 and 2 mixtures of Kullback-Leibler divergences as cost functions in dimensionality reduction based on similarity preservation. Neurocomputing 112, 92–108 (2013)CrossRefGoogle Scholar

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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Universidad Técnica Del NorteIbarraEcuador
  2. 2.Instituto Tecnológico Superior 17 de JulioIbarraEcuador
  3. 3.Universidad de NariñoPastoColombia
  4. 4.Corporación Universitaria Autónoma de NariñoPastoColombia
  5. 5.Universidad SurcolombianaNeiva, HuilaColombia

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