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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Semi-supervised transfer subspace for domain adaptation

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Autor(es):
Pereira, Luis A. M. [1] ; Torres, Ricardo da Silva [1]
Número total de Autores: 2
Afiliação do(s) autor(es):
[1] Univ Campinas UNICAMP, Inst Comp, Sao Paulo - Brazil
Número total de Afiliações: 1
Tipo de documento: Artigo Científico
Fonte: PATTERN RECOGNITION; v. 75, n. SI, p. 235-249, MAR 2018.
Citações Web of Science: 10
Resumo

Domain shift is defined as the mismatch between the marginal probability distributions of a source (training set) and a target domain (test set). A successful research line has been focusing on deriving new source and target feature representations to reduce the domain shift problem. This task can be modeled as a semi-supervised domain adaptation. However, without exploiting at the same time the knowledge available on the labeled source, labeled target, and unlabeled target data, semi-supervised methods are prone to fail. Here, we present a simple and effective Semi-Supervised Transfer Subspace (SSTS) method for domain adaptation. SSTS establishes pairwise constraints between the source and labeled target data, besides it exploits the global structure of the unlabeled data to build a domain invariant subspace. After reducing the domain shift by projecting both source and target domain onto this subspace, any classifier can be trained on the source and tested on target. Results on 49 cross-domain problems confirm that SSTS is a powerful mechanism to reduce domain shift. Furthermore, SSTS yields better classification accuracy than state-of-the-art domain adaptation methods. (C) 2017 Elsevier Ltd. All rights reserved. (AU)

Processo FAPESP: 15/09169-3 - Adaptação de domínio com supervisão mínima em problemas multimídia
Beneficiário:Luis Augusto Martins Pereira
Linha de fomento: Bolsas no Brasil - Doutorado
Processo FAPESP: 13/50155-0 - Combining new technologies to monitor phenology from leaves to ecosystems
Beneficiário:Leonor Patricia Cerdeira Morellato
Linha de fomento: Auxílio à Pesquisa - Programa de Pesquisa sobre Mudanças Climáticas Globais - PITE
Processo FAPESP: 13/50169-1 - Towards an understanding of tipping points within tropical South American biomes
Beneficiário:Ricardo da Silva Torres
Linha de fomento: Auxílio à Pesquisa - Parceria para Inovação Tecnológica - PITE