Support Spinor Machine

TL;DR

This paper introduces the support spinor machine, a novel extension of support vector machines using spinor fields and tensor structures, designed for analyzing nonlinear, nonstationary time series data.

Contribution

It generalizes support vector machines to support spinor and tensor machines using advanced mathematical structures, enabling better analysis of complex time series.

Findings

Support spinor machine performs well on physiological time series classification.

The method effectively handles nonlinear and nonstationary data.

Algorithm implementation uses Holo-Hilbert amplitude modulation.

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.