![](/images/graphics-bg.png)
Domain Adaption Based on ELM Autoencoder
Joint Authors
Deng, Wan-Yu
Qu, Yu-Tao
Zhang, Qian
Source
Mathematical Problems in Engineering
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-06-19
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
We propose a new ELM Autoencoder (ELM-AE) based domain adaption algorithm which describes the subspaces of source and target domain by ELM-AE and then carries out subspace alignment to project different domains into a common new space.
By leveraging nonlinear approximation ability and efficient one-pass learning ability of ELM-AE, the proposed domain adaption algorithm can efficiently seek a better cross-domain feature representation than linear feature representation approaches such as PCA to improve domain adaption performance.
The widely experimental results on Office/Caltech-256 datasets show that the proposed algorithm can achieve better classification accuracy than PCA subspace alignment algorithm and other state-of-the-art domain adaption algorithms in most cases.
American Psychological Association (APA)
Deng, Wan-Yu& Qu, Yu-Tao& Zhang, Qian. 2017. Domain Adaption Based on ELM Autoencoder. Mathematical Problems in Engineering،Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1189440
Modern Language Association (MLA)
Deng, Wan-Yu…[et al.]. Domain Adaption Based on ELM Autoencoder. Mathematical Problems in Engineering No. 2017 (2017), pp.1-8.
https://search.emarefa.net/detail/BIM-1189440
American Medical Association (AMA)
Deng, Wan-Yu& Qu, Yu-Tao& Zhang, Qian. Domain Adaption Based on ELM Autoencoder. Mathematical Problems in Engineering. 2017. Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1189440
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1189440