Abstract
We propose a method of dimension reduction of high dimensional time series data by reservoir computing. The proposed method is a generalization of random projection techniques to time series, which uses a reservoir smaller than input time series. We demonstrate the method by echo state networks for artificially generated time series data. We also discuss an implementation as physical reservoirs and its application of the proposed method to IoT edge computing, which is the first proposal for industry application of physical reservoir computing beyond standard benchmark tasks.
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Yamane, T. et al. (2018). Dimensionality Reduction by Reservoir Computing and Its Application to IoT Edge Computing. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11301. Springer, Cham. https://doi.org/10.1007/978-3-030-04167-0_58
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DOI: https://doi.org/10.1007/978-3-030-04167-0_58
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