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Common Subspace Learning via Cross-Domain Extreme Learning Machine

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Abstract

Extreme learning machine (ELM) is proposed for solving a single-layer feed-forward network (SLFN) with fast learning speed and has been confirmed to be effective and efficient for pattern classification and regression in different fields. ELM originally focuses on the supervised, semi-supervised, and unsupervised learning problems, but just in the single domain. To our best knowledge, ELM with cross-domain learning capability in subspace learning has not been exploited very well. Inspired by a cognitive-based extreme learning machine technique (Cognit Comput. 6:376–390, 1; Cognit Comput. 7:263–278, 2.), this paper proposes a unified subspace transfer framework called cross-domain extreme learning machine (CdELM), which aims at learning a common (shared) subspace across domains. Three merits of the proposed CdELM are included: (1) A cross-domain subspace shared by source and target domains is achieved based on domain adaptation; (2) ELM is well exploited in the cross-domain shared subspace learning framework, and a new perspective is brought for ELM theory in heterogeneous data analysis; (3) the proposed method is a subspace learning framework and can be combined with different classifiers in recognition phase, such as ELM, SVM, nearest neighbor, etc. Experiments on our electronic nose olfaction datasets demonstrate that the proposed CdELM method significantly outperforms other compared methods.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant 61401048, the Fundamental Research Funds for the Central Universities, and Chongqing University Postgraduates’ Innovation Project (No.CYB15030).

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Authors and Affiliations

  1. College of Communication Engineering, Chongqing University, No.174 Shazheng street, Shapingba district, Chongqing, 400044, China

    Yan Liu, Lei Zhang, Pingling Deng & Zheng He

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  1. Yan Liu
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  2. Lei Zhang
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  3. Pingling Deng
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  4. Zheng He
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Correspondence to Lei Zhang.

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Yan Liu, Lei Zhang, Pingling Deng, and Zheng He declare that they have no conflict of interest.

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Liu, Y., Zhang, L., Deng, P. et al. Common Subspace Learning via Cross-Domain Extreme Learning Machine. Cogn Comput 9, 555–563 (2017). https://doi.org/10.1007/s12559-017-9473-5

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  • DOI: https://doi.org/10.1007/s12559-017-9473-5

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