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Tobacco index data grade identification method based on data dimension reduction method

A technology for index data and data dimension reduction, applied in the field of level recognition, can solve problems such as high dimensionality, difficulty in tobacco leaf data level and quality, and achieve the effects of enhancing mining capabilities, optimizing projection directions, and enhancing stability

Pending Publication Date: 2022-01-14
HUNAN NORMAL UNIVERSITY
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Problems solved by technology

The main characteristics of tobacco leaf data are many categories, high dimensionality, non-linearity, etc. Among them, different types of data may also overlap each other, which makes it very difficult to analyze the grade and quality of tobacco leaf data. Therefore, we propose a Tobacco leaf index data grade recognition method based on data dimensionality reduction method extracts the main information in high-dimensional data for analysis and research of tobacco leaf quality workers

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  • Tobacco index data grade identification method based on data dimension reduction method
  • Tobacco index data grade identification method based on data dimension reduction method
  • Tobacco index data grade identification method based on data dimension reduction method

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Embodiment 1

[0050] like Figure 1-2 As shown, the present invention provides a technical solution: a tobacco leaf index data grade identification method based on a data dimensionality reduction method, comprising the following steps:

[0051] Step 1: Read the tobacco leaf index data sample set, and standardize the data set to obtain the D-dimensional spatial data set X={x 1 , x 2 ,...,x N}.

[0052] Step 2: For the D-dimensional spatial data set X={x 1 , x 2 ,...,x N} for data dimensionality reduction.

[0053] Step 3: Use the nearest neighbor classifier to classify the dimensionally reduced data. If the classification match is correct, return 1; if the classification match is wrong, return 0.

[0054] In the step 2, the data dimension reduction processing method is to use the global and local kernel margin discriminant analysis algorithm for the D-dimensional space data set X={x 1 , x 2 ,...,x N} for dimensionality reduction and clustering, which specifically includes the follo...

Embodiment 2

[0106] like figure 1 and 3 As shown, the present invention provides a technical solution: a tobacco leaf index data grade identification method based on a data dimensionality reduction method, comprising the following steps:

[0107] Step 1: Read the tobacco leaf index data sample set, and standardize the data set to obtain the D-dimensional spatial data set X={x 1 , x 2 ,...,x N}.

[0108] Step 2: For the D-dimensional spatial data set X={x 1 , x 2 ,...,x N} for data dimensionality reduction.

[0109] Step 3: Use the nearest neighbor classifier to classify the dimensionally reduced data. If the classification match is correct, return 1; if the classification match is wrong, return 0.

[0110] The data dimensionality reduction processing method in the step 2 is to use the weighted maximum class boundary criterion algorithm to reduce the D-dimensional space data set X={x 1 , x 2 ,...,x N} Inter-class overlap, including the following steps:

[0111] Step 201: Robust ...

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Abstract

The invention belongs to the technical field of data dimensionality reduction, and discloses a tobacco index data grade identification method based on a data dimensionality reduction method. The method comprises the following steps: 1, reading a tobacco index data sample set, and standardizing the data set to obtain a dimensional space data set; 2, carrying out data dimension reduction processing on the dimension space data set; and 3, classifying the data after dimension reduction by using a nearest neighbor classifier, and if classification matching is correct, returning to the step 1, and if the classification matching is wrong, returning to 0. According to the tobacco index data grade identification method based on the data dimension reduction method, great help is provided for grade identification of the tobacco index data, and the grade identification process of the tobacco index data can be rapidly carried out.

Description

technical field [0001] The invention relates to the technical field of tobacco leaf identification, in particular to a tobacco leaf index data level identification method based on a data dimensionality reduction method. Background technique [0002] Data dimensionality reduction can reduce data dimensions and required storage space, remove redundant variables, and improve the efficiency and accuracy of information processing. Traditional data dimensionality reduction methods can be divided into two categories, one is linear dimensionality reduction methods, mainly including principal component analysis and linear discriminant analysis methods, which are used to deal with dimensionality reduction of linear structured data; the other is nonlinear dimensionality reduction Dimensional methods mainly include kernel methods and manifold learning methods. The kernel method mainly retains the global properties of the data, but cannot take into account the local properties of the da...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/2132G06F18/23G06F18/24147
Inventor 唐强任沙王言肖柳明杨森匡凯吕志盛陈志强于朝明建忠穆克圩
Owner HUNAN NORMAL UNIVERSITY
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