Abstract
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.
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References
Ward, M.O., Grinstein, G., Keim, D.: Interactive Data Visualization: Foundations, Techniques, and Applications. CRC Press, Boca Raton (2010)
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)
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 2016
Alvarado-Pérez, J.C., Peluffo-Ordóñez, D.H., Therón, R.: Visualización y métodos kernel: integrando inteligencia natural y artificial (2016)
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)
Dastan, M.: The role of visual perception in data visualization. J. Vis. Lang. Comput. 13(6), 601–622 (2002)
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)
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)
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
Borg, I., Groenen, P.J.: Modern Multidimensional Scaling: Theory and Applications. Springer Science & Business Media, New York (2005)
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
Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput. 15(6), 1373–1396 (2003)
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 2016
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)
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)
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)
Levkowitz, H.: Color Theory and Modeling for Computer Graphics, Visualization, and Multimedia Applications. Springer, New York (1997)
Dix, A.: Human-Computer Interaction. Springer, New York (2009)
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)
Aknowledgments
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.
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Rosero-Montalvo, P.D., Peña-Unigarro, D.F., Peluffo, D.H., Castro-Silva, J.A., Umaquinga, A., Rosero-Rosero, E.A. (2017). Data Visualization Using Interactive Dimensionality Reduction and Improved Color-Based Interaction Model. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) Biomedical Applications Based on Natural and Artificial Computing. IWINAC 2017. Lecture Notes in Computer Science(), vol 10338. Springer, Cham. https://doi.org/10.1007/978-3-319-59773-7_30
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