%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2017/09.04.21.52
%2 sid.inpe.br/sibgrapi/2017/09.04.21.52.38
%T Visualization, kernels and subspaces: a practical study
%D 2017
%A Barbosa, Adriano Oliveira,
%A Nonato, Luis Gustavo,
%@affiliation ICMC-USP/FACET-UFGD
%@affiliation ICMC-USP
%E Torchelsen, Rafael Piccin,
%E Nascimento, Erickson Rangel do,
%E Panozzo, Daniele,
%E Liu, Zicheng,
%E Farias, Mylène,
%E Viera, Thales,
%E Sacht, Leonardo,
%E Ferreira, Nivan,
%E Comba, João Luiz Dihl,
%E Hirata, Nina,
%E Schiavon Porto, Marcelo,
%E Vital, Creto,
%E Pagot, Christian Azambuja,
%E Petronetto, Fabiano,
%E Clua, Esteban,
%E Cardeal, Flávio,
%B Conference on Graphics, Patterns and Images, 30 (SIBGRAPI)
%C Niterói, RJ, Brazil
%8 17-20 Oct. 2017
%I Sociedade Brasileira de Computação
%J Porto Alegre
%S Proceedings
%K kernel methods, subspace clustering, multidimensional projection, visualization.
%X 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.
%@language en
%3 compressed.pdf