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@InProceedings{BenatoTeleFalc:2018:SeLeIn,
               author = "Benato, B{\'a}rbara Caroline and Telea, Alexandru Cristian and 
                         Falc{\~a}o, Alexandre Xavier",
          affiliation = "{University of Campinas} and {University of Groningen} and 
                         {University of Campinas}",
                title = "Semi-Supervised Learning with Interactive Label Propagation guided 
                         by Feature Space Projections",
            booktitle = "Proceedings...",
                 year = "2018",
               editor = "Ross, Arun and Gastal, Eduardo S. L. and Jorge, Joaquim A. and 
                         Queiroz, Ricardo L. de and Minetto, Rodrigo and Sarkar, Sudeep and 
                         Papa, Jo{\~a}o Paulo and Oliveira, Manuel M. and Arbel{\'a}ez, 
                         Pablo and Mery, Domingo and Oliveira, Maria Cristina Ferreira de 
                         and Spina, Thiago Vallin and Mendes, Caroline Mazetto and Costa, 
                         Henrique S{\'e}rgio Gutierrez and Mejail, Marta Estela and Geus, 
                         Klaus de and Scheer, Sergio",
         organization = "Conference on Graphics, Patterns and Images, 31. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Semi-Supervised Learning, Interactive Label Propagation, 
                         Auto-Encoder Neural Networks, Visual Analytics.",
             abstract = "While the number of unsupervised samples for data annotation is 
                         usually high, the absence of large supervised train- ing sets for 
                         effective feature learning and design of high-quality classifiers 
                         is a known problem whenever specialists are required for data 
                         supervision. By exploring the feature space of supervised and 
                         unsupervised samples, semi-supervised learning approaches can 
                         usually improve the classification system. However, these 
                         approaches do not usually exploit the pattern-finding power of the 
                         users visual system during machine learning. In this paper, we 
                         incorporate the user in the semi-supervised learning process by 
                         letting the feature space projection of unsupervised and 
                         supervised samples guide the label propagation actions of the user 
                         to the unsupervised samples. We show that this procedure can 
                         significantly reduce user effort while improving the quality of 
                         the classifier on unseen test sets. Due to the limited number of 
                         supervised samples, we also propose the use of auto-encoder neural 
                         networks for feature learning. For validation, we compare the 
                         classifiers that result from the proposed approach with the ones 
                         trained from the supervised samples only and semi-supervised 
                         trained using automatic label propagation.",
  conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
      conference-year = "Oct. 29 - Nov. 1, 2018",
             language = "en",
           targetfile = "PID5546009.pdf",
        urlaccessdate = "2020, Nov. 25"
}


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