@InProceedings{LevadaHadd:2021:EnLaEi,
               author = "Levada, Alexandre L. M. and Haddad, Michel F. C.",
          affiliation = "Computing Department, Federal University of S{\~a}o Carlos, 
                         Brazil  and Department of Land Economy, University of Cambridge 
                         and School of Business and Management, Queen Mary University of 
                         London, United Kingdom",
                title = "Entropic Laplacian eigenmaps for unsupervised metric learning",
            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 = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Unsupervised metric learning, dimensionality reduction, Laplacian 
                         Eigenmaps, KL-divergence, manifold learning.",
             abstract = "Unsupervised metric learning is concerned with building adaptive 
                         distance functions prior to pattern classification. Laplacian 
                         eigenmaps consists of a manifold learning algorithm which uses 
                         dimensionality reduction to find more compact and meaningful 
                         representations of datasets through the Laplacian matrix of 
                         graphs. In the present paper, we propose the entropic Laplacian 
                         eigenmaps (ELAP) algorithm, a parametric approach that employs the 
                         KullbackLeibler (KL-) divergence between patches of the KNN graph 
                         instead of the pointwise Euclidean metric as the cost function for 
                         the graph weights. Our objective with such a modification is 
                         increasing the robustness of Laplacian eigenmaps against noise and 
                         outliers. Our results using various real-world datasets indicate 
                         that the proposed method is capable of generating more reasonable 
                         clusters while reporting greater classification accuracies 
                         compared to existing widely adopted methods for dimensionality 
                         reduction-based metric learning.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
                  doi = "10.1109/SIBGRAPI54419.2021.00049",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI54419.2021.00049",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45BP6RE",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45BP6RE",
           targetfile = "example.pdf",
        urlaccessdate = "2024, May 06"
}