TY - GEN
T1 - Domain-shared group-sparse dictionary learning for unsupervised domain adaptation
AU - Yang, Baoyao
AU - Ma, Andy J.
AU - YUEN, Pong Chi
N1 - Funding Information:
This work was supported by the Science Faculty Research Grant of Hong Kong Baptist University, Hong Kong Research Grants Council General Research Fund: RGC/HKBU12202514 and SYSU Research Funding: 67000-18821116.
PY - 2018
Y1 - 2018
N2 - Unsupervised domain adaptation has been proved to be a promising approach to solve the problem of dataset bias. To employ source labels in the target domain, it is required to align the joint distributions of source and target data. To do this, the key research problem is to align conditional distributions across domains without target labels. In this paper, we propose a new criterion of domain-shared group-sparsity that is an equivalent condition for conditional distribution alignment. To solve the problem in joint distribution alignment, a domain-shared group-sparse dictionary learning method is developed towards joint alignment of conditional and marginal distributions. A classifier for target domain is trained using the domain-shared group-sparse coefficients and the target-specific information from the target data. Experimental results on cross-domain face and object recognition show that the proposed method outperforms eight state-of-the-art unsupervised domain adaptation algorithms.
AB - Unsupervised domain adaptation has been proved to be a promising approach to solve the problem of dataset bias. To employ source labels in the target domain, it is required to align the joint distributions of source and target data. To do this, the key research problem is to align conditional distributions across domains without target labels. In this paper, we propose a new criterion of domain-shared group-sparsity that is an equivalent condition for conditional distribution alignment. To solve the problem in joint distribution alignment, a domain-shared group-sparse dictionary learning method is developed towards joint alignment of conditional and marginal distributions. A classifier for target domain is trained using the domain-shared group-sparse coefficients and the target-specific information from the target data. Experimental results on cross-domain face and object recognition show that the proposed method outperforms eight state-of-the-art unsupervised domain adaptation algorithms.
UR - http://www.scopus.com/inward/record.url?scp=85047298984&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85047298984
T3 - 32nd AAAI Conference on Artificial Intelligence, AAAI 2018
SP - 7453
EP - 7460
BT - 32nd AAAI Conference on Artificial Intelligence, AAAI 2018
PB - AAAI press
T2 - 32nd AAAI Conference on Artificial Intelligence, AAAI 2018
Y2 - 2 February 2018 through 7 February 2018
ER -