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The objective of the emergent research area, so-called interactive data visualization (IntDataVis), is to link the field of dimensionality reduction (DR) with that of information visualization (IV), in order to harness the special properties of the latter within DR frameworks. In particular, the properties of controllability and interactivity are of interest, which should make the DR outcomes significantly more understandable and tractable for the (no-necessarily-expert neither data analytics nor the nature of data) user. These two properties provide the user with freedom to select the best way for representing the input data. In this talk, a brief overview of some remarkable works on IntDataVis is outlined, with special interest on a benchmark framework interactive model followed from a mixture of different DR techniques’ outcomes along with a generalized kernel-based formulation of spectral DR. Also, trends and open issues are discussed.


Diego Hernán Peluffo-Ordóñez
Prof. Diego Peluffo was born in Pasto – Colombia in 1986. He received his degree in electronic engineering, the M.Eng. and the ​Ph.D.​ degree in industrial automation from the Universidad Nacional de Colombia, Manizales – Colombia, in 2008, 2010 and 2013, respectively. He undertook his doctoral internship at KU Leuven – Belgium. ​Afterward​, he worked as a post-doc at Université Catholique de Louvain at Louvain la-Neuve, Belgium. In 2014, he worked as an assistant teacher at Universidad Cooperativa de Colombia – Pasto. From 2015 to 2017, he worked as a researcher/professor at Universidad Técnica del Norte – Ecuador.
Currently, he is working as a professor at the School of Mathematical Sciences and Information Technology, and researcher at the Computational Intelligence Research Line from Yachay Tech – Ecuador. He is a ​supervisor and external member of ALEPHSYS (Algorithms embedded in Physical Systems) research group from Universitat Rovira i Virgila – Spain. He is invited lecturer at Corporación Universitaria Autónoma de Nariño – Colombia, and member of the SEDMATEC Research Group. He is the head of the SDAS Research Group.
His main research interests are kernel-based and spectral methods for multiple-annotators-driven classification, data clustering​, ​ and dimensionality reduction. His applications of interest encompass analysis of complex high-dimensional data, signals, imaging and videos analysis for medical and industry scenarios.

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