- Tytuł:
- Finding robust transfer features for unsupervised domain adaptation
- Autorzy:
-
Gao, Depeng
Wu, Rui
Liu, Jiafeng
Fan, Xiaopeng
Tang, Xianglong - Powiązania:
- https://bibliotekanauki.pl/articles/331356.pdf
- Data publikacji:
- 2020
- Wydawca:
- Uniwersytet Zielonogórski. Oficyna Wydawnicza
- Tematy:
-
unsupervised domain adaptation
feature reduction
generalized eigenvalue decomposition
object recognition
adaptacja domeny
redukcja cech
rozkład wartości własnych
rozpoznawanie obiektu - Opis:
- 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.
- Źródło:
-
International Journal of Applied Mathematics and Computer Science; 2020, 30, 1; 99-112
1641-876X
2083-8492 - Pojawia się w:
- International Journal of Applied Mathematics and Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł