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Support Spinor Machine

Author

Listed:
  • Kabin Kanjamapornkul
  • Richard Pinv{c}'ak
  • Sanphet Chunithpaisan
  • Erik Bartov{s}

Abstract

We generalize a support vector machine to a support spinor machine by using the mathematical structure of wedge product over vector machine in order to extend field from vector field to spinor field. The separated hyperplane is extended to Kolmogorov space in time series data which allow us to extend a structure of support vector machine to a support tensor machine and a support tensor machine moduli space. Our performance test on support spinor machine is done over one class classification of end point in physiology state of time series data after empirical mode analysis and compared with support vector machine test. We implement algorithm of support spinor machine by using Holo-Hilbert amplitude modulation for fully nonlinear and nonstationary time series data analysis.

Suggested Citation

  • Kabin Kanjamapornkul & Richard Pinv{c}'ak & Sanphet Chunithpaisan & Erik Bartov{s}, 2017. "Support Spinor Machine," Papers 1709.03943, arXiv.org.
    • Handle: RePEc:arx:papers:1709.03943
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    File URL: http://arxiv.org/pdf/1709.03943
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    Cited by:

    1. Mostafa Shabani & Dat Thanh Tran & Juho Kanniainen & Alexandros Iosifidis, 2022. "Augmented Bilinear Network for Incremental Multi-Stock Time-Series Classification," Papers 2207.11577, arXiv.org.
    2. Kanjamapornkul, Kabin & Pinčák, Richard & Bartoš, Erik, 2020. "Cohomology theory for financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 546(C).

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