@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 = "29 Oct.-1 Nov. 2018",
doi = "10.1109/SIBGRAPI.2018.00057",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2018.00057",
language = "en",
ibi = "8JMKD3MGPAW/3RNN9JH",
url = "http://urlib.net/ibi/8JMKD3MGPAW/3RNN9JH",
targetfile = "PID5546009.pdf",
urlaccessdate = "2024, Apr. 29"
}