@InProceedings{GattoFukuJśniSant:2021:AdSuLe,
               author = "Gatto, Bernardo B. and Fukui, Kazuhiro and J{\'u}nior, Waldir S. 
                         S. and Santos, Eulanda M. dos",
          affiliation = "{Federal University of Amazonas} and {University of Tsukuba} and 
                         {Federal University of Amazonas} and {Federal University of 
                         Amazonas}",
                title = "Advances in subspace learning and its applications",
            booktitle = "Proceedings...",
                 year = "2021",
               editor = "Paiva, Afonso and Menotti, David and Baranoski, Gladimir V. G. and 
                         Proen{\c{c}}a, Hugo Pedro and Junior, Antonio Lopes Apolinario 
                         and Papa, Jo{\~a}o Paulo and Pagliosa, Paulo and dos Santos, 
                         Thiago Oliveira and e S{\'a}, Asla Medeiros and da Silveira, 
                         Thiago Lopes Trugillo and Brazil, Emilio Vital and Ponti, Moacir 
                         A. and Fernandes, Leandro A. F. and Avila, Sandra",
         organization = "Conference on Graphics, Patterns and Images, 34. (SIBGRAPI)",
            publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
              address = "Porto Alegre",
             keywords = "Subspace representation, shallow networks, manifold learning, 
                         tensor analysis.",
             abstract = "Pattern-set matching refers to a class of problems where learning 
                         takes place through sets rather than elements. Much used in 
                         computer vision, this approach presents robustness to variations 
                         such as illumination, intrinsic parameters of the signal capture 
                         devices, and pose of the analyzed object. Inspired by applications 
                         of subspace analysis, three new collections of methods are 
                         presented in this thesis\$^{1}\$ summary: (1) New 
                         representations for two-dimensional sets; (2) Shallow networks for 
                         image classification; and (3) Tensor data representation by 
                         subspaces. New representations are proposed to preserve the 
                         spatial structure and maintain a fast processing time. We also 
                         introduce a technique to keep temporal structure, even using the 
                         principal component analysis, which classically does not model 
                         sequences. In shallow networks, we present two convolutional 
                         neural networks that do not require backpropagation, employing 
                         only subspaces for their convolution filters. These networks 
                         present advantages when the training time and hardware resources 
                         are scarce. Finally, to handle tensor data, such as videos, we 
                         propose methods that employ subspaces for representation in a 
                         compact and discriminative way. Our proposed work has been applied 
                         in problems other than computer vision, such as representation and 
                         classification of bioacoustics and text patterns.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45DFJGB",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45DFJGB",
           targetfile = "sibgrapi_camera_ready.pdf",
        urlaccessdate = "2024, May 07"
}