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Principal component analysis enterprise supplier evaluation method based on hybrid weight kernel

A technology of nuclear principal component analysis and evaluation method, which is applied in the field of objective comprehensive evaluation of enterprise suppliers based on mixed weight nuclear principal component analysis, which can solve the problems of scattered index contribution rate and achieve comprehensive evaluation results and remarkable dimensionality reduction effects

Inactive Publication Date: 2017-07-14
NORTHEAST FORESTRY UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the relationship between real indicators is often non-linear, and it is inappropriate to use the traditional linear PCA method for indicators with little linear correlation, and the contribution rate of each indicator may even be too scattered.

Method used

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  • Principal component analysis enterprise supplier evaluation method based on hybrid weight kernel
  • Principal component analysis enterprise supplier evaluation method based on hybrid weight kernel
  • Principal component analysis enterprise supplier evaluation method based on hybrid weight kernel

Examples

Experimental program
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Effect test

Embodiment 1

[0046] A method for evaluating enterprise suppliers based on mixed weight kernel principal component analysis, wherein the representation of eigenvectors in the kernel principal component analysis includes the following specific steps:

[0047] Known data set x 1 ,x 2 ,...x M , where x i ∈ R N ,i=1,2,...,M. M is the total number of samples, and N is the total number of indicators. Mercer kernel function K:R N × R N →R, according to Mercer's theorem, there exists a mapping Φ:R N →R F , such that K(x i ,x j ) = Φ(x i ) T Φ(x j ). F is the kernel space dimension. PCA is in R N As discussed in , then the kernel PCA is mapped in R after F discussed in the space. That is, discuss the mapped data set Φ(x 1 ),Φ(x 2 ),…Φ(x M ), in R F Principal component analysis in .

[0048] We know that the main part of discussing principal component analysis is to find the covariance matrix and its eigenvalues ​​and eigenvectors, but we actually use eigenvectors.

[0049] Co...

Embodiment 2

[0063] A method for evaluating enterprise suppliers based on mixed weight kernel principal component analysis, wherein the calculation of eigenvectors and eigenvalues ​​in the kernel principal component analysis includes the following specific steps:

[0064] Since Cv=λv, so (Ψ(x k )) T Cv=λ(Ψ(x k )) T v

[0065] Bundle Bring in:

[0066] Right formula:

[0067]

[0068] Left type:

[0069]

[0070] Note that k, j=1, 2, .

[0071]

[0072] in

[0073]

[0074] So:

[0075]

[0076] The eigenvectors of yes eigenvalues ​​of . Or

Embodiment 3

[0078] A method for evaluating enterprise suppliers based on mixed weight kernel principal component analysis, wherein the calculation method of the kernel space projection sample kernel matrix after averaging in the kernel principal component analysis includes the following specific steps:

[0079] Let I ∈ R M × R M , I ij =1,i=1,2,...,M,j,k,l=1,2,...,M

[0080]

[0081] where K ij =Φ(x i ) T Φ(x j ), K is the kernel matrix, so:

[0082]

[0083]

[0084] So far, it has been asked Then the eigenvector You can ask for it.

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Abstract

A method for evaluating enterprise suppliers based on mixed weight kernel principal component analysis, characterized in that: the method includes the following steps: (1) determining the supplier evaluation criteria, and classifying the output and input indicators; (2) the step ( 1) Take the reciprocal of the obtained input indicators and normalize all indicators using the maximum and minimum method, so that the range is between [0, 1]; (3) Check the normalized data obtained in step (2) Matrix calculation, calculate the averaged kernel space sample kernel matrix and find its eigenvalues ​​and eigenvectors, normalize the eigenvectors, select the eigenvectors with a cumulative contribution rate greater than 80% to form an orthogonal basis matrix; (4) The samples obtained in step (2) are projected, the mixed weight vector is calculated, and the corresponding projection coordinates are weighted and summed; (5) The comprehensive indicators obtained in step (4) are sorted in reverse order. The application of the present invention is applied to enterprise supplier evaluation.

Description

[0001] Technical field: [0002] The application of the present invention relates to the field of supplier evaluation, in particular to an objective comprehensive evaluation method based on mixed weight kernel principal component analysis of enterprise suppliers. [0003] Background technique: [0004] At present, with the development of economy and the improvement of modernization level, supply chain management has been paid more and more attention by many enterprises. As an important issue in supply chain management, the selection and evaluation of suppliers has become more important. How to make a comprehensive, objective and true evaluation of suppliers is crucial to ensuring the quality of material supply, reducing procurement costs, and improving the overall quality of production enterprises. Benefits have very important practical significance. Supplier evaluation, also known as supplier selection, began in 1966 with a paper published by American scholar Dickson on "Purc...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06
CPCG06Q10/0639
Inventor 陶新民常瑞王若彤王立海沈微孙术发陶思睿
Owner NORTHEAST FORESTRY UNIVERSITY
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