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@InProceedings{BarbosaNona:2017:PrSt,
               author = "Barbosa, Adriano Oliveira and Nonato, Luis Gustavo",
          affiliation = "ICMC-USP/FACET-UFGD and ICMC-USP",
                title = "Visualization, kernels and subspaces: a practical study",
            booktitle = "Proceedings...",
                 year = "2017",
               editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and 
                         Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and 
                         Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba, 
                         Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo 
                         and Vital, Creto and Pagot, Christian Azambuja and Petronetto, 
                         Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
         organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "kernel methods, subspace clustering, multidimensional projection, 
                         visualization.",
             abstract = "Data 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.",
  conference-location = "Niter{\'o}i, RJ",
      conference-year = "Oct. 17-20, 2017",
             language = "en",
                  ibi = "8JMKD3MGPAW/3PJ5RDH",
                  url = "http://urlib.net/rep/8JMKD3MGPAW/3PJ5RDH",
           targetfile = "compressed.pdf",
        urlaccessdate = "2021, Jan. 26"
}


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