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Sample classification method based on weighted PTSVM (projection twin support vector machine)

A technology of support vector machine and classification method, which is applied in the field of non-parallel hyperplane classifiers, and can solve problems such as not considering local identification information

Inactive Publication Date: 2016-03-30
YANCHENG INST OF TECH
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Problems solved by technology

Therefore, this method does not consider the local discriminative information contained between samples

Method used

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  • Sample classification method based on weighted PTSVM (projection twin support vector machine)
  • Sample classification method based on weighted PTSVM (projection twin support vector machine)
  • Sample classification method based on weighted PTSVM (projection twin support vector machine)

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Embodiment

[0062] like figure 1 As shown, the method includes the following steps:

[0063] Step 1: Construct a neighbor graph G within each type of sample and between different types of samples s and G d ;

[0064] The second step: according to the neighbor graph G of each type of sample s Calculate sample weights;

[0065] The third step: Calculate the weighted mean center of each type of sample on the basis of the second step;

[0066] Step 4: Determine the opposite class sample that is closer to the specific class sample according to the inter-class neighbor graph;

[0067] Step 5: Use the results of steps 1, 2, 3, and 4 to construct an optimization problem in linear mode;

[0068] Step 6: Solve the dual problem of the optimization problem in the fifth step, and obtain the decision hyperplane of the two types of samples: x T w 1 +b 1 = 0 and x T w 2 +b 2 = 0;

[0069] Step 7: Classify unknown samples according to the decision hyperplane in step 6.

[0070] For each type...

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Abstract

The invention discloses as ample classification method based on a weighted PTSVM (projection twin support vector machine), and the method comprises the following steps: respectively constructing in-class neighbor graphs Gs and an inter-class neighbor graph Gd in all sample classes and among different sample classes; calculating sample weights according to the in-class neighbor graphs Gs of all sample classes, and calculating the weighted mean center of each sample class; determining a reverse sample class, which is nearer to a specific sample class, according to the inter-class neighbor graph Gd, and constructing an optimization problem in a linear mode; solving a dual problem of the optimization problem, obtaining the decision hyperplanes of two classes of samples: xTw1+b1=0 and xTw2+b2=0, and carrying out the classification of unknown samples according to the decision hyperplanes, wherein w1 and w2 are respectively the projection axes of the first and second classes of samples, x represents samples in an n-dimensional vector space, and b1 and b2 respectively represent the biases of the decision hyperplanes of two classes of samples. The method improves the local learning capability of an algorithm to a certain extent, and greatly reduces the solving complexity of the algorithm.

Description

technical field [0001] The invention relates to a non-parallel hyperplane classifier method, in particular to a sample classification method for a support vector machine based on weighted projection. Background technique [0002] For binary classification problems, the traditional support vector machine (SVM) generates classification hyperplanes based on the principle of large intervals, but the disadvantages are that the calculation complexity is high and the distribution of samples is not fully considered. In recent years, as one of the expansion directions of SVM, nonparallel hyperplane classifiers (NHCs) represented by twin support vector machine (TWSVM) are gradually becoming a new research hotspot in the field of pattern recognition. The idea of ​​TWSVM originates from the generalized eigenvalue approximate support vector machine (generalizedeigenvalueproximalSVM, GEPSVM), which converts the GEPSVM problem into two smaller-scale quadratic programming problems like SVM,...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/2411
Inventor 花小朋孙一颗
Owner YANCHENG INST OF TECH
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