Unsupervised Domain Adaptation Using Exemplar-SVMs with Adaptation Regularization
Joint Authors
He, Yiwei
Tian, Yingjie
Tang, Jingjing
Ma, Yue
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-04-22
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Domain adaptation has recently attracted attention for visual recognition.
It assumes that source and target domain data are drawn from the same feature space but different margin distributions and its motivation is to utilize the source domain instances to assist in training a robust classifier for target domain tasks.
Previous studies always focus on reducing the distribution mismatch across domains.
However, in many real-world applications, there also exist problems of sample selection bias among instances in a domain; this would reduce the generalization performance of learners.
To address this issue, we propose a novel model named Domain Adaptation Exemplar Support Vector Machines (DAESVMs) based on exemplar support vector machines (exemplar-SVMs).
Our approach aims to address the problems of sample selection bias and domain adaptation simultaneously.
Comparing with usual domain adaptation problems, we go a step further in slacking the assumption of i.i.d.
First, we formulate the DAESVMs training classifiers with reducing Maximum Mean Discrepancy (MMD) among domains by mapping data into a latent space and preserving properties of original data, and then, we integrate classifiers to make a prediction for target domain instances.
Our experiments were conducted on Office and Caltech10 datasets and verify the effectiveness of the model we proposed.
American Psychological Association (APA)
He, Yiwei& Tian, Yingjie& Tang, Jingjing& Ma, Yue. 2018. Unsupervised Domain Adaptation Using Exemplar-SVMs with Adaptation Regularization. Complexity،Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1136178
Modern Language Association (MLA)
He, Yiwei…[et al.]. Unsupervised Domain Adaptation Using Exemplar-SVMs with Adaptation Regularization. Complexity No. 2018 (2018), pp.1-13.
https://search.emarefa.net/detail/BIM-1136178
American Medical Association (AMA)
He, Yiwei& Tian, Yingjie& Tang, Jingjing& Ma, Yue. Unsupervised Domain Adaptation Using Exemplar-SVMs with Adaptation Regularization. Complexity. 2018. Vol. 2018, no. 2018, pp.1-13.
https://search.emarefa.net/detail/BIM-1136178
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1136178