Semi-Supervised Learning with Interactive Label Propagation guided by Feature Space Projections

Autor(es)

Benato, Barbara C. ; Telea, Alexandru C. ; Falcao, Alexandre X. ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: PROCEEDINGS 2018 31ST SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI); v. N/A, p. 8-pg., 2018-01-01.

Resumo

While the number of unsupervised samples for data annotation is usually high, the absence of large supervised training 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 user's 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. (AU)

• Processo FAPESP: 16/25776-0 - Otimização de redes neurais Autocodificadoras guiada por análise visual de dados
• Beneficiário: Bárbara Caroline Benato
• Modalidade de apoio: Bolsas no Brasil - Mestrado
• Processo FAPESP: 14/12236-1 - AnImaLS: Anotação de Imagem em Larga Escala: o que máquinas e especialistas podem aprender interagindo?
• Beneficiário: Alexandre Xavier Falcão
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 17/25327-3 - Análise visual para propagação de rótulo assistida pelo usuário no projeto de classificador de imagens baseado em redes neurais
• Beneficiário: Bárbara Caroline Benato
• Modalidade de apoio: Bolsas no Exterior - Estágio de Pesquisa - Mestrado