@InProceedings{CervatiNetoLeva:2020:PaApUn,
               author = "Cervati Neto, Alaor and Levada, Alexandre Luis Magalh{\~a}es",
          affiliation = "{Federal University of Sa\̃o Carlos} and {Federal University 
                         of Sa\̃o Carlos}",
                title = "ISOMAP-KL: a parametric approach for unsupervised metric 
                         learning",
            booktitle = "Proceedings...",
                 year = "2020",
               editor = "Musse, Soraia Raupp and Cesar Junior, Roberto Marcondes and 
                         Pelechano, Nuria and Wang, Zhangyang (Atlas)",
         organization = "Conference on Graphics, Patterns and Images, 33. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "pattern recognition, manifold learning.",
             abstract = "Unsupervised metric learning consists in building data-specific 
                         similarity measures without information of the class labels. 
                         Dimensionality reduction (DR) methods have shown to be a powerful 
                         mathematical tool for uncovering the underlying geometric 
                         structure of data. Manifold learning algorithms are capable of 
                         finding a more compact representation for data in the presence of 
                         non-linearities. However, one limitation is that most of them are 
                         pointwise methods, in the sense that they are not robust to the 
                         presence of outliers and noise in data. In this paper, we present 
                         ISOMAP-KL, a parametric patch-based algorithm that uses the 
                         KL-divergence between local Gaussian distributions learned from 
                         neighborhood systems along the KNN graph. We use this 
                         non-Euclidean measure to compute the weights and define the 
                         entropic KNN graph, whose shortest paths approximate the geodesic 
                         distances between patches of points in a parametric feature space. 
                         Results obtained in several datasets show that the proposed method 
                         is capable of improving the classification accuracy in comparison 
                         to other DR methods.",
  conference-location = "Porto de Galinhas (virtual)",
      conference-year = "7-10 Nov. 2020",
                  doi = "10.1109/SIBGRAPI51738.2020.00046",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI51738.2020.00046",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/43BAC95",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/43BAC95",
           targetfile = "PID6629767.pdf",
        urlaccessdate = "2024, Apr. 29"
}