Abstract
In recent times, an undeniable fact is that the amount of data available has increased dramatically due mainly to the advance of new technologies allowing for storage and communication of enormous volumes of information. In consequence, there is an important need for finding the relevant information within the raw data through the application of novel data visualization techniques that permit the correct manipulation of data. This issue has motivated the development of graphic forms for visually representing and analyzing high-dimensional data. Particularly, in this work, we propose a graphical approach, which, allows the combination of dimensionality reduction (DR) methods using an angle-based model, making the data visualization more intelligible. Such approach is designed for a readily use, so that the input parameters are interactively given by the user within a user-friendly environment. The proposed approach enables users (even those being non-experts) to intuitively select a particular DR method or perform a mixture of methods. The experimental results prove that the interactive manipulation enabled by the here-proposed model-due to its ability of displaying a variety of embedded spaces-makes the task of selecting a embedded space simpler and more adequately fitted for a specific need.
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Acknowledgments
This work is supported by the Smart Data Analysis Systems (SDAS) Research Group (http://sdas-group.com), as well as the “Grupo de Investigación en Ingeniería Eléctrica y Electrónica - GIIEE” from Universidad de Nariño. Also, 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. 18 November 1st, 2016 by VIPRI from Universidad de Nariño.
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Basante-Villota, C.K. et al. (2018). Angle-Based Model for Interactive Dimensionality Reduction and Data Visualization. In: Hernández Heredia, Y., Milián Núñez, V., Ruiz Shulcloper, J. (eds) Progress in Artificial Intelligence and Pattern Recognition. IWAIPR 2018. Lecture Notes in Computer Science(), vol 11047. Springer, Cham. https://doi.org/10.1007/978-3-030-01132-1_17
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