基于钉板分布稀疏变分自编码器的异常检测算法研究

陈华华; 陈哲

doi:10.11959/j.issn.1000-0801.2022238

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基于钉板分布稀疏变分自编码器的异常检测算法研究

研究与开发 | 更新时间：2024-06-05

- 基于钉板分布稀疏变分自编码器的异常检测算法研究
- Research on anomaly detection algorithm based on sparse variational autoencoder using spike and slab prior
- 电信科学 2022年38卷第12期页码：65-77
- 作者机构：
- 作者简介：
  
  [ "陈华华（1975- ），男，博士，杭州电子科技大学副教授，主要研究方向为数字图像处理、计算机视觉和模式识别" ]
  [ "陈哲（1995- ），男，杭州电子科技大学硕士生，主要研究方向为异常检测" ]
- 基金信息：
  
  浙江省“领雁”研发攻关计划项目;The “Leading Goose” Technologies Research and Development Program of Zhejiang Province(2022C03065)
- DOI：10.11959/j.issn.1000-0801.2022238
  中图分类号： TP393
- 网络出版日期：2022-12，
  
  纸质出版日期：2022-12-20
- 稿件说明：
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陈华华, 陈哲. 基于钉板分布稀疏变分自编码器的异常检测算法研究[J]. 电信科学, 2022,38(12):65-77.

Huahua CHEN, Zhe CHEN. Research on anomaly detection algorithm based on sparse variational autoencoder using spike and slab prior[J]. Telecommunications science, 2022, 38(12): 65-77.
陈华华, 陈哲. 基于钉板分布稀疏变分自编码器的异常检测算法研究[J]. 电信科学, 2022,38(12):65-77. DOI： 10.11959/j.issn.1000-0801.2022238.

Huahua CHEN, Zhe CHEN. Research on anomaly detection algorithm based on sparse variational autoencoder using spike and slab prior[J]. Telecommunications science, 2022, 38(12): 65-77. DOI： 10.11959/j.issn.1000-0801.2022238.

摘要

异常检测由于其广泛的应用一直是数据挖掘中一个重要的研究分支，它有助于研究人员获得重要的信息进而对数据做出更好的决策。提出了一种基于钉板分布稀疏变分自编码器的异常检测模型。首先，使用离散-连续混合模型钉板分布作为变分自编码器的先验，模拟隐变量所在空间的稀疏性，得到数据特征的稀疏表示；其次，以所提出的自编码器构建深度支持向量网络，对特征空间进行压缩，并采用最优超球体区分正常数据和异常数据；再次，以数据特征和超球体中心之间的欧氏距离完成异常检测；最后，在基准数据集MNIST （modifiednational institute of standards and technology database）和Fashion-MNIST上的实验评估表明，与现存的异常检测算法相比，本文所提出的算法具有更好的检测效果。

Abstract

Anomaly detection remains to be an essential and extensive research branch in data mining due to its widespread use in a wide range of applications.It helps researchers to obtain vital information and make better decisions about data by detecting abnormal data.Considering that sparse coding can get more powerful features and improve the performance of other tasks

an anomaly detection model based on sparse variational autoencoder was proposed.Firstly

the discrete mixed modelspike and slab distribution was used as the prior of variational autoencoder

simulated the sparsity of the space where the hidden variables were located

and obtained the sparse representation of data characteristics.Secondly

combined with the deep support vector network

the feature space was compressed

and the optimal hypersphere was found to discriminate normal data and abnormal data.And then

the abnormal fraction of the data was measured by the Euclidean distance from the data feature to the center of the hypersphere

and then the abnormal detection was carried out.Finally

the algorithm was evaluated on the benchmark datasets MNIST and Fashion-MNIST

and the experimental results show that the proposed algorithm achieves better effects than the state-of-the-art methods.

关键词

Keywords

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