Adversarial network data generation method and abnormal event detection method

Abstract

The invention discloses an adversarial network data generation method and an abnormal event detection method. The method is mainly designed for a multi-dimensional data set abnormal event detection scene in the field of current artificial intelligence and data mining. The generative adversarial network objective function and the corresponding optimization mode are desgined by using the exponentialinformation measure, thereby guiding the generative adversarial network to generate data. Furthermore, the conventional event data and the abnormal event data in the multi-dimensional data set are classified based on the exponential information measure generative adversarial network, so that the classification error is reduced, and the abnormal event detection performance is improved.

Description

technical field [0001] The present invention relates to the scene of abnormal event detection in the fields of artificial intelligence and data mining, in particular to the training of generating an adversarial network based on exponential information measurement and its application in abnormal event detection of multi-dimensional feature data sets. Background technique [0002] With the development of artificial intelligence technology and the advent of the era of big data, people's demand for how to mine more valuable information hidden in large amounts of data is becoming stronger and increasing day by day. Today, the development trend of data information processing technology has transformed from the original analysis and processing of low-dimensional isomorphic data to the mining and processing of high-dimensional heterogeneous data. In this regard, as one of the mainstream methods of current data information processing, artificial neural networks have been widely used ...

AI Technical Summary

Problems solved by technology

In particular, when there are small probability types of data in the data set, the relatively infrequent data generation effect is not good

In this regard, it is a possible breakthrough point to consider formulating an objective function based on a new type of information measure to optimize the data generation effect and improve the anomaly detection results of the generative adversarial network, and the relevant research is relatively rare at present.

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