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An Improved Auto-encoder Based on 2-Level Prioritized Experience Replay for High Dimension Skewed Data

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Proceedings of the 23rd Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES 2019)

Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 12))

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Abstract

Auto-encoder as the representative method for data dimensionality reduction and feature extraction, plays a very important role on machine learning. However, the data in the actual research work or industrial production are not always normalized data, at this time, it will lead high reconstruction error and slow convergence speed. This study proposed an improved auto-encoder and a denoising auto-encoder based on 2-level prioritized experience replay, which can improve accuracy and reduce loss, while processing a dimensionality reduction or feature extraction problem on high dimension skewed data. In order to evaluate the effectiveness of the proposed method, three models of high dimension simulation dataset which on different skewed degrees are generated. The results of evaluation experiments show that the proposed method can get lower reconstruction error than conventional method for high dimension skewed simulation data.

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Acknowledgment

Thanks to my professor Tomoki Hamagami, for helping me a lot in this research. Thanks to the China Scholarship Council (CSC) in 2016, pursue my study in Japan as a Ph.D. student under File no. 201608140085.

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Authors and Affiliations

  1. Graduate School of Engineering, Yokohama National University, 79-5 Tokiwadai, Hodogaya-ku, Yokohama, Kanagawa, 240-8501, Japan

    Xin Li & Tomoki Hamagami

Authors
  1. Xin Li
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  2. Tomoki Hamagami
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Corresponding author

Correspondence to Xin Li .

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Editors and Affiliations

  1. Department of Computer Science, National Defense Academy of Japan, Yokosuka-shi, Japan

    Hiroshi Sato

  2. Faculty of Maritime Safety Technology, Japan Coast Guard Academy, Wakabacho, Japan

    Saori Iwanaga

  3. Department of Applied Mathematics and Physics, Tottori University, Tottori, Japan

    Akira Ishii

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Li, X., Hamagami, T. (2020). An Improved Auto-encoder Based on 2-Level Prioritized Experience Replay for High Dimension Skewed Data. In: Sato, H., Iwanaga, S., Ishii, A. (eds) Proceedings of the 23rd Asia Pacific Symposium on Intelligent and Evolutionary Systems. IES 2019. Proceedings in Adaptation, Learning and Optimization, vol 12. Springer, Cham. https://doi.org/10.1007/978-3-030-37442-6_9

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