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		<doi>10.1109/SIBGRAPI.2011.22</doi>
		<citationkey>InabaSallRaub:2011:KeSaMa</citationkey>
		<title>Kernel Sammon Map</title>
		<format>DVD, On-line.</format>
		<year>2011</year>
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		<author>Inaba, Fernando K.,</author>
		<author>Salles, Evandro O. T.,</author>
		<author>Rauber, Thomas W.,</author>
		<affiliation>Departamento de Engenharia Elétrica, Centro Tecnológico, Universidade Federal do Espírito Santo</affiliation>
		<affiliation>Departamento de Engenharia Elétrica, Centro Tecnológico, Universidade Federal do Espírito Santo</affiliation>
		<affiliation>Departamento de Informática, Centro Tecnológico, Universidade Federal do Espírito Santo</affiliation>
		<editor>Lewiner, Thomas,</editor>
		<editor>Torres, Ricardo,</editor>
		<e-mailaddress>thomas@inf.ufes.br</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 24 (SIBGRAPI)</conferencename>
		<conferencelocation>Maceió, AL, Brazil</conferencelocation>
		<date>28-31 Aug. 2011</date>
		<publisher>IEEE Computer Society</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Full Paper</tertiarytype>
		<transferableflag>1</transferableflag>
		<versiontype>finaldraft</versiontype>
		<keywords>Sammon map, Hilbert feature space, Kernels, Visualization, Kernel Principal Component Analysis.</keywords>
		<abstract>We extend the visualization technique of highdimensional patterns conceived by Sammon to the case when the patterns have been previously mapped to an implicitly defined Hilbert feature space in which distances can be measured by kernels. The principal benefit of our technique is the possibility to gain insight into the distribution of the patterns, even in this generally non-accessible feature space.</abstract>
		<language>en</language>
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