A survey of multi-source domain adaptation

Sun, Shiliang; Shi, Honglei; Wu, Yuanbin

doi:10.1016/j.inffus.2014.12.003

Cited by 281 publications

(176 citation statements)

References 26 publications

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“…If we only concern the similarity at the level of "domaindomain", namely, set all A is 's equal to 1, (MS) 2 TL would be similar to the DAM algorithm [6]. Thus, we compare our method (MS) 2 TL with DAM in the experiments.…”

Section: Performance Of Our Proposed Methodsmentioning

confidence: 99%

“…Thus, we compare our method (MS) 2 TL with DAM in the experiments. LS-SVM classifier is pre-learned in every source domains and used as the source classifiers f s 's.…”

Section: Performance Of Our Proposed Methodsmentioning

confidence: 99%

“…The accuracies of (MS) 2 TL and DAM generally increase along with the increasing of n (the number of the labeled target samples per class). The performance of (MS) 2 TL is better when Naïve Bayes is used as f s 's rather than LS-SVM. However, the difference in performance between the two cases becomes smaller as n increases.…”

Section: Performance Of Our Proposed Methodsmentioning

confidence: 99%

“…To minimize the loss of the labeled target data as well as the multisource transfer manifold regularizer defined on the unlabeled target data simultaneously, the proposed framework (MS) 2 TL is then formulated as follows…”

Section: Multi-similarity Based Multi-source Transfer Learning and Itmentioning

confidence: 99%

“…Transfer learning [1], [2] can effectively exploit and transfer the knowledge from different but similar source domains for target domain learning, which has been applied in many real-world applications. For the single-source domain setting, much work has been developed [1].…”

Section: Introductionmentioning

confidence: 99%

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Learning from Multiple Sources via Multiple Domain Relationship

Liu¹,

Yang²,

Liu³

et al. 2016

IEICE Trans. Inf. & Syst.

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SUMMARYTransfer learning extracts useful information from the related source domain and leverages it to promote the target learning. The effectiveness of the transfer was affected by the relationship among domains. In this paper, a novel multi-source transfer learning based on multisimilarity was proposed. The method could increase the chance of finding the sources closely related to the target to reduce the "negative transfer" and also import more knowledge from multiple sources for the target learning. The method explored the relationship between the sources and the target by multi-similarity metric. Then, the knowledge of the sources was transferred to the target based on the smoothness assumption, which enforced that the target classifier shares similar decision values with the relevant source classifiers on the unlabeled target samples. Experimental results demonstrate that the proposed method can more effectively enhance the learning performance.

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Section: Performance Of Our Proposed Methodsmentioning

confidence: 99%

“…Thus, we compare our method (MS) 2 TL with DAM in the experiments. LS-SVM classifier is pre-learned in every source domains and used as the source classifiers f s 's.…”

Section: Performance Of Our Proposed Methodsmentioning

confidence: 99%

Section: Performance Of Our Proposed Methodsmentioning

confidence: 99%

Section: Multi-similarity Based Multi-source Transfer Learning and Itmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Learning from Multiple Sources via Multiple Domain Relationship

Liu¹,

Yang²,

Liu³

et al. 2016

IEICE Trans. Inf. & Syst.

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Adaptive soft sensor based on transfer learning and ensemble learning for multiple process states

Yamada

Kaneko

2022

Analytical Science Advances

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The objective of this study is to develop an adaptive software sensor technique that can predict objective process variables for a target grade in a plant while also considering information related to various other grades. We use a dataset of the target grade as the target domain and those of the other grades as source domains to perform transfer learning. Multiple models or sub-models are constructed by setting a source domain for each grade and changing the number of samples used as the source domain. Furthermore, to prevent the negative transfer, the use of a source domain is automatically judged. In this study, we constructed sub-models using the locally weighted partial least squares approach as an adaptive soft sensor technique. The values of an objective variable were predicted with ensemble learning using sub-models. The effectiveness of the proposed method was verified using a dataset measured in an actual incineration plant, and the proposed method was able to accurately predict the product quality even when the plant was operated in five grades and when a new grade was produced.

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A novel wind turbine health condition monitoring method based on common features distribution adaptation

Liu

Ren

2020

Int J Energy Res

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A survey of multi-source domain adaptation

Cited by 281 publications

References 26 publications

Learning from Multiple Sources via Multiple Domain Relationship

Learning from Multiple Sources via Multiple Domain Relationship

Adaptive soft sensor based on transfer learning and ensemble learning for multiple process states

A novel wind turbine health condition monitoring method based on common features distribution adaptation

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