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Lightweight Modal Regression for Stand Alone Embedded Systems

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 11954)

Abstract

Although the CPU power of recent embedded systems has increased, their storage space is still limited. To overcome this limitation, most embedded devices are connected to a cloud server so they can outsource heavy calculations. However, some applications must handle private data, meaning internet connections are undesirable based on security concerns. Therefore, small devices that handle private data should be able to work without internet connections. This paper presents a limited modal regression model that restricts the number of internal units to a certain fixed number. Modal regression can be used for multivalued function approximation with limited sensory inputs. In this study, a kernel density estimator (KDE) with a fixed number of kernels called “limited KDE” was constructed. We will demonstrate how to implement the limited KDE and how to construct a lightweight algorithm for modal regression using a system-on-chip field-programmable gate array device.

Keywords

  • Multivalued function
  • Regression
  • Modal regression
  • Kernel Density Estimator (KDE)
  • Incremental learning

A part of this research was supported by a Special grant of Chubu University.

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Notes

  1. 1.

    Note that Gaussian kernel satisfies the condition for the reproduction kernel.

  2. 2.

    Remember that \(\varvec{X}\) in Eq. (2) includes the output dimension and \(\varvec{X} = [\varvec{x}, y]^T\).

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Correspondence to Koichiro Yamauchi .

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Watanabe, T., Yamauchi, K. (2019). Lightweight Modal Regression for Stand Alone Embedded Systems. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Lecture Notes in Computer Science(), vol 11954. Springer, Cham. https://doi.org/10.1007/978-3-030-36711-4_31

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  • DOI: https://doi.org/10.1007/978-3-030-36711-4_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36710-7

  • Online ISBN: 978-3-030-36711-4

  • eBook Packages: Computer ScienceComputer Science (R0)