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Reference TypeConference Paper (Conference Proceedings)
Last Update2017:
Metadata Last Update2020: administrator
Citation KeyBarbosaNona:2017:PrSt
TitleVisualization, kernels and subspaces: a practical study
DateOct. 17-20, 2017
Access Date2021, Jan. 19
Number of Files1
Size602 KiB
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Author1 Barbosa, Adriano Oliveira
2 Nonato, Luis Gustavo
EditorTorchelsen, Rafael Piccin
Nascimento, Erickson Rangel do
Panozzo, Daniele
Liu, Zicheng
Farias, Mylène
Viera, Thales
Sacht, Leonardo
Ferreira, Nivan
Comba, João Luiz Dihl
Hirata, Nina
Schiavon Porto, Marcelo
Vital, Creto
Pagot, Christian Azambuja
Petronetto, Fabiano
Clua, Esteban
Cardeal, Flávio
Conference NameConference on Graphics, Patterns and Images, 30 (SIBGRAPI)
Conference LocationNiterói, RJ
Book TitleProceedings
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Tertiary TypeMaster's or Doctoral Work
History2017-09-04 21:52:38 :: -> administrator ::
2020-02-20 22:06:47 :: administrator -> :: 2017
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Keywordskernel methods, subspace clustering, multidimensional projection, visualization.
AbstractData involved in real applications are usually spread around in distinct subspaces which may have different dimensions. We would like to study how the subspace structure information can be used to improve visualization tasks. On the other hand, what if the data is tangled in this high-dimensional space, how to visualize its patterns or how to accomplish classification tasks? This paper presents an study for both problems pointed out above. For the former, we use subspace clustering techniques to define, when it exists, a subspace structure, studying how this information can be used to support visualization tasks based on multidimensional projections. For the latter problem we employ kernel methods, well known in the literature, as a tool to assist visualization tasks. We use a similarity measure given by the kernel to develop a completely new multidimensional projection technique capable of dealing with data embedded in the implicit feature space defined by the kernel.
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Next Higher Units8JMKD3MGPAW/3PJT9LS
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