Abstract
This work describes a new model for interactive data visualization followed from a dimensionality-reduction (DR)-based approach. Particularly, the mixture of the resulting spaces of DR methods is considered, which is carried out by a weighted sum. For the sake of user interaction, corresponding weighting factors are given via an intuitive color-based interface. Also, to depict the DR outcomes while showing information about the input high-dimensional data space, the low-dimensional representations reached by the mixture is conveyed using scatter plots enhanced with an interactive data-driven visualization. In this connection, a constrained dissimilarity approach define the graph to be drawn on the scatter plot.
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Acknowledgments
This work is supported by the “Grupo de Investigación en Ingeniería Eléctrica y Electrónica - GIIEE” from Universidad de Nariño. As well, the authors acknowledge to the research project “Desarrollo de una metodología de visualización interactiva y eficaz de información en Big Data” supported by Agreement No. 180 November 1st, 2016 by VIPRI from Universidad de Nariño, as well as Universidad Técnica del Norte – Ecuador.
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Peña-Unigarro, D.F. et al. (2017). Interactive Data Visualization Using Dimensionality Reduction and Dissimilarity-Based Representations. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2017. IDEAL 2017. Lecture Notes in Computer Science(), vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_50
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DOI: https://doi.org/10.1007/978-3-319-68935-7_50
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