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Method and system for constructing a graph semi-supervised learning model

A semi-supervised learning and model technology, applied in character and pattern recognition, instruments, computer components, etc.

Pending Publication Date: 2020-09-01
TSINGHUA UNIV +1
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

This results in a much larger amount of unlabeled data than labeled data

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  • Method and system for constructing a graph semi-supervised learning model
  • Method and system for constructing a graph semi-supervised learning model
  • Method and system for constructing a graph semi-supervised learning model

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Embodiment Construction

[0018] Embodiments of the present invention will be described below with reference to the drawings. In the following description, numerous specific details are set forth in order to provide a more complete understanding of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced without some of these specific details. Furthermore, it should be understood that the invention is not limited to the particular embodiments described. Instead, it is conceivable to implement the present invention in any combination of the following features and elements, regardless of whether they relate to different embodiments. Accordingly, the following aspects, features, embodiments and advantages are by way of illustration only and should not be considered as elements or limitations of the appended claims unless expressly stated in the claims.

[0019] In the era of big data, supervised learning has achieved great success in many...

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Abstract

The invention discloses a method for constructing a graph semi-supervised learning model, a computer system and a computer readable storage medium. The method comprises the following steps of creatinga graph structure by utilizing the distance between any two training samples in a plurality of training samples, wherein the graph structure is used as an updated graph structure; for the updated graph structure, circularly executing the following steps: obtaining a sub-graph structure in the updated graph structure; obtaining an optimal k value of the sub-graph structure within a specified selectable k value range by utilizing the distance between any two training samples in the plurality of training samples; updating the sub-graph structure according to the optimal k value so as to obtain agraph structure updated again; and in response to the fact that one sub-graph structure of the graph structure after being updated again does not need to be obtained, training a graph semi-supervisedlearning model corresponding to the graph structure after being updated again.

Description

technical field [0001] The present invention relates to machine learning, in particular, the present invention relates to a method for constructing a graph semi-supervised learning model, a computer system and a computer-readable storage medium. [0002] technical background [0003] In the era of big data, supervised learning has achieved great success in many fields, such as face recognition, personalized recommendation, etc. However, in many fields, such as the medical field, the cost of data annotation is much higher than the cost of data acquisition. This results in a much larger amount of unlabeled data than labeled data. Semi-supervised learning can effectively utilize both labeled and unlabeled data to improve classification performance. One of the commonly used semi-supervised learning models is the graph semi-supervised learning model. In the prior art, graph semi-supervised learning models are widely used in real-world applications, such as medical data, image d...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/2155G06F18/24
Inventor 刘世霞陈长建王兆伟李宇峰
Owner TSINGHUA UNIV
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