Spectral data dimension reduction method for optimizing kernel independent components based on sparrow search algorithm

Abstract

The invention discloses a spectral data dimension reduction method for optimizing kernel independent components based on a sparrow search algorithm. The spectral data dimension reduction method comprises the following steps: acquiring near infrared spectral data of a tested sample; selecting a kernel function, determining a kernel parameter optimization problem and constructing a fitness function;setting initial parameters of the sparrow population, and generating an initial position matrix and a fitness matrix of the sparrow population; updating the optimal position and the optimal fitness value of the sparrow population according to the predation and anti-predation behaviors of the sparrows; applying the optimal kernel parameter matrix meeting the convergence condition to a kernel function; performing spheroidization decomposition preprocessing on the spectral data; and solving an unmixing matrix in independent component analysis to realize effective dimensionality reduction of spectral data. According to the method, blindness of kernel function parameter selection in a spectral data dimension reduction process by applying a kernel independent component analysis method is avoided, effective dimension reduction of near-infrared spectrum high-dimensional nonlinear data is realized, and the method is excellent in performance, high in practicability, stable in calculation process and easy to realize.

Description

technical field [0001] The invention belongs to the technical field of near-infrared spectral data, in particular to a method for reducing the dimensionality of spectral data based on a sparrow search algorithm to optimize kernel independent components. Background technique [0002] In recent years, near-infrared spectral analysis technology has been widely used in the field of fruit quality detection due to its advantages of rapidity, non-contact, non-damage, and no secondary pollution. Near-infrared spectroscopy is a method for identifying substances and determining their chemical composition and relative content based on their absorption, reflection or scattering spectra. Since the near-infrared spectrum contains a large number of spectral bands and the bands of each component overlap seriously, only using single-wavelength spectral data to establish a prediction or discrimination model will produce large errors, while using full-spectrum band data for modeling is time-co...

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Problems solved by technology

[0005] In order to overcome the shortcomings of the above-mentioned prior art, the object of the present invention is to provide a spectral data dimensionality reduction method based on the sparrow search algorithm to optimize the kernel independent component. The random parameters of the function are optimized, which can not only remove redundant information and noise information, but also retain the intrinsic structural characteristics of the data, thereby improving the accuracy of spectral data feature extraction and the efficiency of nuclear independent component analysis, and solving the problem of near-infrared spectroscopy due to high Difficulty in extracting key features of data caused by shortcomings such as dimensionality, spectral band overlap, and unknown spectral distribution structure

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