Research on anomaly detection algorithm based on sparse variational autoencoder using spike and slab prior

Huahua CHEN; Zhe CHEN

doi:10.11959/j.issn.1000-0801.2022238

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Research on anomaly detection algorithm based on sparse variational autoencoder using spike and slab prior

Research and Development | 更新时间：2024-06-05

- Research on anomaly detection algorithm based on sparse variational autoencoder using spike and slab prior
- Telecommunications Science Vol. 38, Issue 12, Pages: 65-77(2022)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2022238
  CLC： TP393
- Published Online：2022-12，
  
  Published：20 December 2022
- 稿件说明：
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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：

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

references

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