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A semi-supervised classification method for graph transduction

A classification method, semi-supervised technology, applied in the field of data processing, which can solve the problem of lack of accuracy of classification results

Active Publication Date: 2020-08-04
NORTHWEST UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Both the harmonic Gaussian field algorithm and the local and global consistency algorithm rely too much on the initial label set. If the graph contains noise, or the input data set cannot be classified due to other factors, the classification results obtained by the graph transduction method are not accurate enough. sex

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  • A semi-supervised classification method for graph transduction
  • A semi-supervised classification method for graph transduction
  • A semi-supervised classification method for graph transduction

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Embodiment Construction

[0040] Such as figure 1 and figure 2 Shown, the present invention comprises the following steps:

[0041] Step 1, obtain video image information: video image sensor 1 collects video images and transmits the obtained video images to computer 2, and computer 2 stores the obtained video images into the total sample set X, the number of sample points in the total sample set X It is n×h, both n and h are positive integers not less than 2;

[0042] It should be noted that the video image includes a two-dimensional color image and a two-dimensional black and white image.

[0043] Step 2, select the marked points on the video image: the sample points in the total sample set X are divided into category C according to the category, and the computer 2 selects the marked sample points on the video image, and the marked sample points are included in the category For all categories of , computer 2 stores the marked sample points into the marked sample set X l , labeled sample set X l ...

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Abstract

The invention discloses a graph transfer semi-supervised classification method, comprising the following steps: step 1, acquiring video image information; step 2, selecting marked points on the video image; step 3, selecting preselected samples from unmarked sample points points; Step 4, classify the pre-selected sample points; Step 5, classify the unmarked sample points. The invention pre-selects the unmarked sample points, and then classifies the pre-selected sample points by calculating the similarity of the samples, reduces the false connection between the pre-selected sample points, and then reduces the time for composing the picture, and utilizes the sample categories of the marked sample points and The sample similarity between the marked sample point and the unmarked sample point can get the classification result of the unmarked sample point, which solves the problem of dependence on the marked sample set and improves the classification accuracy.

Description

technical field [0001] The invention belongs to the technical field of data processing, and in particular relates to a graph transduction semi-supervised classification method. Background technique [0002] Currently, supervised learning, unsupervised learning, and semi-supervised learning algorithms are the three most popular learning algorithms. Based on the fact that there are only a small number of labeled samples in the massive data in the fields of images and models in reality, making full use of labeled data and unlabeled data for classification learning has become a more mainstream research method, which has also created a semi-supervised learning algorithm in classification. The hottest position in the algorithm. The semi-supervised learning algorithm has two branches, namely the inductive learning algorithm and the transduction learning algorithm. Among them, whether to generate a classifier is the biggest difference between the two algorithms. Specifically, indu...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62
CPCG06F18/2155G06F18/24137
Inventor 王娜王小凤耿国华宋倩楠
Owner NORTHWEST UNIV