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Mathematical Problems in Engineering
Volume 2017, Article ID 1239164, 8 pages
https://doi.org/10.1155/2017/1239164
Research Article

Domain Adaption Based on ELM Autoencoder

School of Computer, Xi’an University of Posts & Telecommunications, Xi’an 710121, China

Correspondence should be addressed to Wan-Yu Deng; moc.621@uynawgned

Received 21 June 2016; Revised 26 November 2016; Accepted 18 May 2017; Published 19 June 2017

Academic Editor: Jason Gu

Copyright © 2017 Wan-Yu Deng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.