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Feature Adaptive Online Sequential Extreme Learning Machine for lifelong indoor localization

  • Extreme Learning Machine and Applications
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Abstract

Wi-Fi-based indoor localization with high capability and feasibility needs to implement lifelong online learning mechanism. However, the characteristic of Wi-Fi is wide variability, which lies in not only the fluctuation of signal strength value, but also the increase or decrease in the number of access points (APs). The traditional algorithms are effective for signal fluctuation, but cannot handle the dimension-changing problem of features caused by increase and decrease in APs’ number. To solve this problem, we propose a Feature Adaptive Online Sequential Extreme Learning Machine (FA-OSELM) algorithm. It can transfer the original model to a new one with a small number of data with new features, so as to make the new model suitable for the new feature dimension. The experiments show that the FA-OSELM can get higher accuracy with a small amount of new data, and it is an effective method to make lifelong indoor localization practical.

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Acknowledgments

This work is supported by Natural Science Foundation of China under Grant Nos. 61173066 and 41201410 and Strategic Emerging Industry Development Special Funds of Guangdong Province under Grant No. 2011912030.

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Authors and Affiliations

  1. Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China

    Xinlong Jiang, Junfa Liu, Yiqiang Chen, Yang Gu & Zhenyu Chen

  2. University of Chinese Academy of Sciences, Beijing, 100190, China

    Xinlong Jiang, Yang Gu & Zhenyu Chen

  3. Xiangtan University, Xiangtan, 411105, Hunan, China

    Dingjun Liu

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  1. Xinlong Jiang
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  2. Junfa Liu
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Correspondence to Xinlong Jiang.

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Jiang, X., Liu, J., Chen, Y. et al. Feature Adaptive Online Sequential Extreme Learning Machine for lifelong indoor localization. Neural Comput & Applic 27, 215–225 (2016). https://doi.org/10.1007/s00521-014-1714-x

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  • DOI: https://doi.org/10.1007/s00521-014-1714-x

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