Domain Adaption Based on ELM Autoencoder

Wan-Yu Deng, Yu-Tao Qu and Qian Zhang

Mathematical Problems in Engineering, 2017, vol. 2017, 1-8

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.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:1239164

DOI: 10.1155/2017/1239164

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