Social security character portrait method based on multi-view learning

A multi-view and portrait technology, applied in the field of data analysis, can solve problems such as difficulty in obtaining prior knowledge and labeled samples, and achieve the effect of accurate mapping

Active Publication Date: 2019-07-23
SHENZHEN RES INST OF WUHAN UNIVERISTY +1
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  • Abstract
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AI Technical Summary

Problems solved by technology

However, in social security application scenarios, it is difficult to obtain complete prior knowledge and labeled samples, and only part of the do...

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  • Social security character portrait method based on multi-view learning
  • Social security character portrait method based on multi-view learning
  • Social security character portrait method based on multi-view learning

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Embodiment Construction

[0013] In order to facilitate those of ordinary skill in the art to understand and implement the present invention, the present invention will be described in further detail below in conjunction with the accompanying drawings and examples of implementation. It should be understood that the implementation examples described here are only used to illustrate and explain the present invention, and are not intended to limit this invention.

[0014] The semi-supervised learning algorithm allows the learner to automatically use a large amount of unlabeled data to assist in the learning of a small amount of labeled data. It is suitable for learning the mapping rules of ternary spatial identity attributes to portrait labels under the condition that complete labeled samples are difficult to obtain. Different identity attributes in the triple space can form multiple views of the same portrait label. For example, the political topic keywords of an object browsing webpages in cyberspace an...

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Abstract

The invention discloses a social security character portrait method based on multi-view learning. In social security application of multi-source identity big data, complete priori knowledge and annotation samples are difficult to obtain, a multi-view cooperative training model based on part of domain knowledge, a small number of annotation samples and a large number of unannotated samples is constructed, and accurate mapping from identity attributes to portrait labels is achieved. The method comprises the following steps: firstly, learning three attribute sub-views including a physical space,a social space and a network space and corresponding weights from ternary space identity attributes through ternary space view decomposition of a labeled sample data set; secondly, portrait label classification is conducted on unlabeled samples through a ternary space multi-view classifier, credible classified samples are generated in combination with domain knowledge voting and added to a labeledsample data set, and labeled samples are enriched; the method has an important application value in the field of social safety.

Description

technical field [0001] The invention belongs to the technical field of data analysis, and relates to a method for portrait of a person, in particular to a method for portrait of a social security person based on multi-view learning. [0002] technical background [0003] Every move of a person will leave digital traces in physical, social and cyberspace; physical space includes surveillance video clips collected by a large number of cameras installed in the city, spatial positions recorded by positioning and navigation equipment, and human-computer interaction behavior data; Space includes phone calls, WeChat, text messages, making friends on social platforms, chatting, shopping records on e-commerce platforms, Weibo, etc. to generate various behavioral data; A large amount of basic data in public security, social conditions and public opinion surveys. [0004] Whether it is general Internet rumors, cyber crimes such as fraud, or violent terrorist crimes with major hazards, ...

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Application Information

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IPC IPC(8): G06K9/62G06Q50/00
CPCG06Q50/01G06F18/214G06F18/2415
Inventor 王中元韩镇唐雪华何政
Owner SHENZHEN RES INST OF WUHAN UNIVERISTY
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