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Type: Artigo
Title: Visualizing And Interacting With Kernelized Data
Author: Barbosa
A.; Paulovich
F. V.; Paiva
A.; Goldenstein
S.; Petronetto
F.; Nonato
L. G.
Abstract: Kernel-based methods have experienced a substantial progress in the last years, tuning out an essential mechanism for data classification, clustering and pattern recognition. The effectiveness of kernel-based techniques, though, depends largely on the capability of the underlying kernel to properly embed data in the feature space associated to the kernel. However, visualizing how a kernel embeds the data in a feature space is not so straightforward, as the embedding map and the feature space are implicitly defined by the kernel. In this work, we present a novel technique to visualize the action of a kernel, that is, how the kernel embeds data into a high-dimensional feature space. The proposed methodology relies on a solid mathematical formulation to map kernelized data onto a visual space. Our approach is faster and more accurate than most existing methods while still allowing interactive manipulation of the projection layout, a game-changing trait that other kernel-based projection techniques do not have.
Subject: Multidimensional Projection
Kernel Methods
Editor: IEEE Computer Soc
Los Alamitos
Citation: Ieee Transactions On Visualization And Computer Graphics. Ieee Computer Soc, v. 22, p. 1314 - 1325, 2016.
Rights: fechado
Identifier DOI: 10.1109/TVCG.2015.2464797
Date Issue: 2016
Appears in Collections:Unicamp - Artigos e Outros Documentos

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