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Reference TypeConference Proceedings
Last Update2018: administrator
Metadata Last Update2020: administrator
Citation KeyBenatoTeleFalc:2018:SeLeIn
TitleSemi-Supervised Learning with Interactive Label Propagation guided by Feature Space Projections
DateOct. 29 - Nov. 1, 2018
Access Date2020, Dec. 02
Number of Files1
Size2297 KiB
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Author1 Benato, Bárbara Caroline
2 Telea, Alexandru Cristian
3 Falcão, Alexandre Xavier
Affiliation1 University of Campinas
2 University of Groningen
3 University of Campinas
EditorRoss, Arun
Gastal, Eduardo S. L.
Jorge, Joaquim A.
Queiroz, Ricardo L. de
Minetto, Rodrigo
Sarkar, Sudeep
Papa, João Paulo
Oliveira, Manuel M.
Arbeláez, Pablo
Mery, Domingo
Oliveira, Maria Cristina Ferreira de
Spina, Thiago Vallin
Mendes, Caroline Mazetto
Costa, Henrique Sérgio Gutierrez
Mejail, Marta Estela
Geus, Klaus de
Scheer, Sergio
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
History2018-08-31 09:43:59 :: -> administrator ::
2020-02-19 03:10:44 :: administrator -> :: 2018
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Is the master or a copy?is the master
Document Stagecompleted
Document Stagenot transferred
Content TypeExternal Contribution
Tertiary TypeFull Paper
KeywordsSemi-Supervised Learning, Interactive Label Propagation, Auto-Encoder Neural Networks, Visual Analytics.
AbstractWhile 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.
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Next Higher Units8JMKD3MGPAW/3RPADUS
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