Trajectory data sorting method based on generative adversarial network

Abstract

The invention discloses a trajectory data sorting method based on a generative adversarial network. The trajectory data sorting method comprises the following steps: inputting real trajectory data into the generative adversarial network so that the generative adversarial network is trained into a generator so as to generate simulated trajectory data which are the same as the real trajectory data in distribution; then using the generator of the generative adversarial network to generate a plurality of groups of simulated trajectory data; and finally carrying out sorting treatment on the generated groups of simulated trajectory data and the real trajectory data together so as to obtain trajectory user mapping. According to the trajectory data sorting method, the distribution of the real trajectory data can be simulated through the generative adversarial network, generated simulated trajectory data and real trajectory data are together used as data sources for sorting trajectory data, andthe trajectory data are sorted, so that the problem of data sparsity can be solved effectively, and the adverse impact generated by sparse trajectory data on trajectory data sorting is avoided; and due to the fact that corresponding trajectory user mapping also exists in the sparse trajectory data, the sorting for the sparse trajectory data can be realized, and the data sorting effect is improved.

Description

technical field [0001] The invention belongs to the field of deep learning in machine learning, and relates to a trajectory data method based on machine learning, in particular to a data processing method for enhancing the trajectory data classification effect based on adversarial learning. Background technique [0002] With the popularity of smartphones and wearable smart devices in human life, more and more location-based social network (LBSNs) data are being mined. Classifying these data according to users is a very important research direction. The classification results are of great use for accurate recommendations of advertisements or location users, and even for tracking missing persons. [0003] Traditional machine learning methods use algorithms such as SVM, LDA, and LCSS to classify data. Today's society is an era of big data. Under the premise of massive data, the consumption of computer memory by traditional machine learning algorithms makes it unbearable for ma...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to overcome the defect that it is difficult to effectively classify sparse data in the trajectory data classification method in the prior art, and provide a trajectory data classification method based on generative adversarial

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