Transfer robust sparse coding based on graph and joint distribution adaption for image representation

Transfer learning can transfer knowledge from a source domain to a target domain, promoting the performance of the model learned from the source data. Sparse coding can make the representation of a model more succinct and easy to manipulate. Existing transfer sparse coding methods assume the data fr...

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Vydáno v:Knowledge-based systems Ročník 147; s. 1 - 11
Hlavní autoři: Zhao, Peng, Wang, Wei, Lu, Yijuan, Liu, Huiting, Yao, Sheng
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 01.05.2018
Elsevier Science Ltd
Témata:
Algorithms
Artificial intelligence
Coding
Data
Distribution
Experiments
Graphical representations
Image representation
Information dissemination
Knowledge management
Knowledge sharing
Learning
Noise
Representation
Robustness
Sparse coding
Transfer learning
Image representation
Sparse coding
Transfer learning
ISSN:0950-7051, 1872-7409
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Shrnutí:Transfer learning can transfer knowledge from a source domain to a target domain, promoting the performance of the model learned from the source data. Sparse coding can make the representation of a model more succinct and easy to manipulate. Existing transfer sparse coding methods assume the data from the source and the target domains are accurate, which can provide useful information. However, in many real applications, the data in the source and target domains may contain noise and useless information, which could severely degrade the performance of the learned model. In this paper, we propose a transfer robust sparse coding based on graph and joint distribution adaption for image representation. The noise matrix model is utilized to handle noise and useless information in the transfer sparse coding. Moreover, the differences of marginal distribution and conditional distribution are simultaneously reduced in the transfer robust sparse coding. Extensive experiments on six benchmark datasets show the proposed method can effectively deal with the noise and useless information and therefore outperforms several state-of-the-art transfer learning methods on cross-distribution domains.
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ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2018.02.011