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Please use this identifier to cite or link to this item: http://hdl.handle.net/10651/33986

Title: Interactive Dimensionality Reduction for Visual Analytics
Author(s): Díaz Blanco, Ignacio
Cuadrado Vega, Abel Alberto
Pérez López, Daniel
García Fernández, Francisco Javier
Verleysen, Michel
Keywords: Data visualization
Dimensionality reduction
Machine learning
Issue date: 2014
Publisher: ESANN
Format extent: p. 183-188
Abstract: In this work, we present a novel approach for data visualiza- tion based on interactive dimensionality reduction (iDR). The main idea of the paper relies on considering for visualization the intermediate results of non-convex DR algorithms under changes on the metric of the input data space driven by the user. With an appropriate visualization interface, our approach allows the user to focus on the relationships among dynamically selected groups of variables, as well as to assess the impact of a single variable or groups of variables in the structure of the data.
Description: ESANN 2014 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (22. 2014. Bruges (Belgium))
URI: http://hdl.handle.net/10651/33986
ISBN: 978-287419095-7
Sponsored: Fnancial support from the Spanish Ministry of Economy (MINECO) and FEDER funds from the EU
Project id.: MINECO
Appears in Collections:Capítulos de libros
Ingeniería Eléctrica, Electrónica, de Computadores y Sistemas

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ESANN2014-Poster.pdfPoster presentado en la conferencia17,86 MBAdobe PDFView/Open

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