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Adaptive LSH based on the particle swarm method with the attractor selection model for fast approximation of Gaussian process regression

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Abstract

Gaussian process regression (GPR) is one of the non-parametric methods and has been studied in many fields to construct a prediction model for highly non-linear system. It has been difficult to apply it to a real-time task due to its high computational cost but recent high-performance computers and computationally efficient algorithms make it possible. In our previous work, we derived a fast approximation method for GPR using a locality-sensitive hashing (LSH) and product of experts model, but its performance depends on the parameters of the hash functions used in LSH. Hash functions are usually determined randomly. In this research, we propose an optimization method for the parameters of hash functions by referring to a swarm optimization method. The experimental results show that accurate force estimation of an actual robotic arm is achieved with high computational efficiency.

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Acknowledgments

This research was supported in part by “Program for Leading Graduate Schools” of the Ministry of Education, Culture, Sports, Science and Technology, Japan.

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Authors and Affiliations

  1. Department of Systems Innovation, Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama-cho, Toyonaka, Osaka, 560-8531, Japan

    Yuya Okadome, Kenji Urai, Yutaka Nakamura & Hiroshi Ishiguro

  2. Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, Suita, Japan

    Tetsuya Yomo

Authors
  1. Yuya Okadome
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  2. Kenji Urai
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  3. Yutaka Nakamura
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  4. Tetsuya Yomo
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  5. Hiroshi Ishiguro
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Corresponding author

Correspondence to Yutaka Nakamura.

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Okadome, Y., Urai, K., Nakamura, Y. et al. Adaptive LSH based on the particle swarm method with the attractor selection model for fast approximation of Gaussian process regression. Artif Life Robotics 19, 220–226 (2014). https://doi.org/10.1007/s10015-014-0161-1

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  • DOI: https://doi.org/10.1007/s10015-014-0161-1

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