Domain Adaption Based on ELM Autoencoder. (19th June 2017)

• Record Type: Journal Article
• Authors: Deng, Wan-Yu; Qu, Yu-Tao; Zhang, Qian
• Other Names: Gu Jason Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2017(2017)
• Publication Date: 2017-06-19
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2017/1239164
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : 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.