A Quadratic Feature Selection Method for Fluorescence Spectroscopy Based on Hybrid Particle Swarm and Continuous Projection

Abstract

The invention discloses a fluorescence spectrum secondary feature selection method based on mixed particle swarm and continuous projection, which is characterized in that: first, the mixed particle swarm algorithm is used to perform the first feature selection on the fluorescence spectrum, and the original high-dimensional fluorescence spectrum is The first dimensionality reduction is achieved by selecting fluorescence spectra with significant features in several dimensions; and then continuing to apply the continuous projection algorithm in the characteristic spectrum to achieve the second feature selection, so as to perform the second dimensionality reduction on the fluorescence spectrum. This method makes full use of the advantages of particle swarm algorithm, genetic algorithm and continuous projection algorithm, and can effectively reduce the dimensionality of fluorescence spectra.

Description

technical field

[0001] The invention belongs to the field of spectral information processing, and relates to a feature dimension reduction processing method of fluorescence spectrum, in particular to a secondary feature selection method of fluorescence spectrum based on mixed particle swarm and continuous projection. Background technique

[0002] The fluorescence spectrum of a substance has the characteristics of high dimensionality and large amount of information. Modeling and quantitative analysis based on the original fluorescence spectrum usually has high accuracy. However, due to the large amount of redundant information in the original spectrum, the model training is complicated and the calculation low efficiency. It is particularly necessary to reduce the dimensionality of the original fluorescence spectrum by using the method of feature selection. Modeling and analysis based on the feature spectrum after dimensionality reduction will greatly speed up the training spe...

Examples

Embodiment 1

[0054] Embodiment 1: Fluorescence spectrum secondary feature selection method based on mixed particle swarm and continuous projection, see Figure 2-Figure 6 , the specific process is as follows:

[0055] 1. If figure 2 As shown, firstly, the hybrid particle swarm optimization algorithm is used to perform the first feature selection on the original fluorescence spectrum. The main process is to use the discrete binary particle swarm optimization algorithm to perform feature selection on the original fluorescence spectrum, in which genetic operations are introduced in the particle update link, including crossover , mutation and other operations to increase the diversity of particles. The process mainly includes the following steps:

[0056] (1) Binary code

[0057] For the original fluorescence spectrum, assuming that N is the number of wavelength points of the fluorescence spectrum, set N+20-dimensional binary codes, the first N indicates whether the wavelength point is sel...

technical field

[0001] The invention belongs to the field of spectral information processing, and relates to a feature dimension reduction processing method of fluorescence spectrum, in particular to a secondary feature selection method of fluorescence spectrum based on mixed particle swarm and continuous projection. Background technique

[0002] The fluorescence spectrum of a substance has the characteristics of high dimensionality and large amount of information. Modeling and quantitative analysis based on the original fluorescence spectrum usually has high accuracy. However, due to the large amount of redundant information in the original spectrum, the model training is complicated and the calculation low efficiency. It is particularly necessary to reduce the dimensionality of the original fluorescence spectrum by using the method of feature selection. Modeling and analysis based on the feature spectrum after dimensionality reduction will greatly speed up the training spe...

Examples

Embodiment 1

[0054] Embodiment 1: Fluorescence spectrum secondary feature selection method based on mixed particle swarm and continuous projection, see Figure 2-Figure 6 , the specific process is as follows:

[0055] 1. If figure 2 As shown, firstly, the hybrid particle swarm optimization algorithm is used to perform the first feature selection on the original fluorescence spectrum. The main process is to use the discrete binary particle swarm optimization algorithm to perform feature selection on the original fluorescence spectrum, in which genetic operations are introduced in the particle update link, including crossover , mutation and other operations to increase the diversity of particles. The process mainly includes the following steps:

[0056] (1) Binary code

[0057] For the original fluorescence spectrum, assuming that N is the number of wavelength points of the fluorescence spectrum, set N+20-dimensional binary codes, the first N indicates whether the wavelength point is sel...

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