<abstract xmlns="http://www.w3.org/1999/xhtml"><p>An insufficient number or lack of training samples is a bottleneck in traditional machine learning and object recognition. Recently, unsupervised domain adaptation has been proposed and then widely applied for cross-domain object recognition, which can utilize the labeled samples from a source domain to improve the classification performance in a target domain where no labeled sample is available. The two domains have the same feature and label spaces but different distributions. Most existing approaches aim to learn new representations of samples in source and target domains by reducing the distribution discrepancy between domains while maximizing the covariance of all samples. However, they ignore subspace discrimination, which is essential for classification. Recently, some approaches have incorporated discriminative information of source samples, but the learned space tends to be overfitted on these samples, because they do not consider the structure information of target samples. Therefore, we propose a feature reduction approach to learn robust transfer features for reducing the distribution discrepancy between domains and preserving discriminative information of the source domain and the local structure of the target domain. Experimental results on several well-known cross-domain datasets show that the proposed method outperforms state-of-the-art techniques in most cases.</p></abstract>

An insufficient number or lack of training samples is a bottleneck in traditional machine learning and object recognition. Recently, unsupervised domain adaptation has been proposed and then widely applied for cross-domain object recognition, which can utilize the labeled samples from a source domain to improve the classification performance in a target domain where no labeled sample is available. The two domains have the same feature and label spaces but different distributions. Most existing approaches aim to learn new representations of samples in source and target domains by reducing the distribution discrepancy between domains while maximizing the covariance of all samples. However, they ignore subspace discrimination, which is essential for classification. Recently, some approaches have incorporated discriminative information of source samples, but the learned space tends to be overfitted on these samples, because they do not consider the structure information of target samples. Therefore, we propose a feature reduction approach to learn robust transfer features for reducing the distribution discrepancy between domains and preserving discriminative information of the source domain and the local structure of the target domain. Experimental results on several well-known cross-domain datasets show that the proposed method outperforms state-of-the-art techniques in most cases.

Finding Robust Transfer Features for Unsupervised Domain Adaptation

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Finding Robust Transfer Features for Unsupervised Domain Adaptation"}

An insufficient number or lack of training samples is a bottleneck in traditional machine learning and object recognition. Recently, unsupervised domain...

Finding Robust Transfer Features for Unsupervised Domain Adaptation

Online veröffentlicht: 03. Apr. 2020

Seitenbereich: 99 - 112

Eingereicht: 05. Juni 2019

Akzeptiert: 30. Nov. 2019

DOI: https://doi.org/10.34768/amcs-2020-0008

Schlüsselwörter
unsupervised domain adaptation, feature reduction, generalized eigenvalue decomposition, object recognition

© 2020 Depeng Gao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Finding Robust Transfer Features for Unsupervised Domain Adaptation

Online veröffentlicht: 03. Apr. 2020

Seitenbereich: 99 - 112

Eingereicht: 05. Juni 2019

Akzeptiert: 30. Nov. 2019

DOI: https://doi.org/10.34768/amcs-2020-0008

Schlüsselwörterunsupervised domain adaptation, feature reduction, generalized eigenvalue decomposition, object recognition

© 2020 Depeng Gao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
unsupervised domain adaptation, feature reduction, generalized eigenvalue decomposition, object recognition