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Multi-label classification learning method based on matching learning

A learning method and multi-label technology, applied in the field of extremely large-scale multi-label classification learning, can solve problems such as not being able to handle long-tail labels well, and achieve the effect of supporting online incremental learning and ensuring parallel learning

Active Publication Date: 2018-07-06
北京东方科诺科技发展有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This type of algorithm uses the assumption of low-rank space and cannot handle long-tail labels well.

Method used

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  • Multi-label classification learning method based on matching learning
  • Multi-label classification learning method based on matching learning
  • Multi-label classification learning method based on matching learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0054] This embodiment provides a very large-scale multi-label classification learning method based on matching learning, combining figure 1 As shown, the specific steps are as follows:

[0055] Step 1. Collect user data on the Internet, including user tags.

[0056] Step 2. Extract features from data such as user text and images, and perform feature value calculation. In order to obtain the data set D={(x 1 ,w 1 ,y 1 )...(x n ,w n ,y n )}. Where x is the feature set, w is the corresponding feature value set, and y is the label set.

[0057] Step 3. Randomly sample a mini-batch from the data set for gradient descent mini-batch, and prepare to optimize the parameters of the multi-label model. The specific steps are as follows:

[0058] Step 301. Perform random shuffling on the data set D.

[0059] Step 302, traverse the shuffled data set with a step size M, and generate a mini-batch D at each step m .

[0060] Step 303, for D m Randomly sample N sets of negative la...

Embodiment 2

[0078] This embodiment provides a multi-label classification learning method based on matching learning, including the following steps:

[0079] S1: Collect client data in the Internet, perform feature value calculation on the client data, and obtain a training set D;

[0080] S2: traverse the training set D, and set the negative label set and positive label set in the training set D;

[0081] S3: Calculate the embedded representation E of the feature set in the training set D;

[0082] S4: Calculate the embedded representation Z of the positive label set + and the embedded representation Z of the set of negative labels - ;

[0083] S5: Comparing the embedded representation E and the embedded representation Z + Perform loss calculation to obtain a positive label loss value, for the embedded representation E and the embedded representation Z - Calculate the loss to get the negative label loss value;

[0084] S6: According to the positive label loss value and the negative ...

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Abstract

The invention provides a multi-label classification learning method based on matching learning. The method comprises the steps that characteristic value calculation is conducted on client data to obtain a training set; positive and negative labels in the training set are set; an embedded representation E of a characteristic set of the training set is calculated; an embedded representation Z+ of the positive label and an embedded representation Z- of the negative label are calculated; loss calculation is conducted on E and Z+, and loss calculation is conducted on E and Z-; a gradient descent algorithm is adopted to perform model training on the training set according to the positive and negative loss values; training is completed when the loss value of the training model is no long decreased, otherwise training is conducted again; finally the training model is tested by using a testing set. According to the method, the adverse effect on traditional extremely large scale multi-label classification learning of long-tail labels is taken into consideration. In addition, a model learning manner of the gradient descent algorithm is adopted to ensure that the model can be expanded to a large scale data set, not only can parallelization learning of the model be ensured, but also online incremental learning of the model can be supported.

Description

technical field [0001] The invention relates to the technical field of machine learning, in particular to an extremely large-scale multi-label classification learning method based on matching learning and neural network models. Background technique [0002] With the rapid development of Web 2.0 technology, a large amount of user-related or even user-generated content has accumulated on the Internet. In a wide variety of massive data, there is a wealth of information that can reflect user characteristics, which is an important data support for user portraits. User profiling has always been an important issue in the research of social computing. User portraits, that is, labeling user information, provide companies with basic information about users, and can help companies quickly find accurate user groups and more extensive feedback information such as user needs. The core task of user portrait is to "label" users. How to use data mining or machine learning to automatically...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/214G06F18/2431
Inventor 翟书杰李晨
Owner 北京东方科诺科技发展有限公司