Research on anomaly detection algorithm based on sparse variational autoencoder using spike and slab prior
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...
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2022-12-01
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| Series: | Dianxin kexue |
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| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2022238/ |
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| _version_ | 1841530788983603200 |
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| author | Huahua CHEN Zhe CHEN |
| author_facet | Huahua CHEN Zhe CHEN |
| author_sort | Huahua CHEN |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ddee3c0135224c52a467d212b2321925 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2022-12-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-ddee3c0135224c52a467d212b23219252025-01-15T02:59:45ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012022-12-0138657759574425Research on anomaly detection algorithm based on sparse variational autoencoder using spike and slab priorHuahua CHENZhe CHENAnomaly 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.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2022238/anomaly detectionvariational autoencoderspike and slab distributiondeep support vector network |
| spellingShingle | Huahua CHEN Zhe CHEN Research on anomaly detection algorithm based on sparse variational autoencoder using spike and slab prior Dianxin kexue anomaly detection variational autoencoder spike and slab distribution deep support vector network |
| title | Research on anomaly detection algorithm based on sparse variational autoencoder using spike and slab prior |
| title_full | Research on anomaly detection algorithm based on sparse variational autoencoder using spike and slab prior |
| title_fullStr | Research on anomaly detection algorithm based on sparse variational autoencoder using spike and slab prior |
| title_full_unstemmed | Research on anomaly detection algorithm based on sparse variational autoencoder using spike and slab prior |
| title_short | Research on anomaly detection algorithm based on sparse variational autoencoder using spike and slab prior |
| title_sort | research on anomaly detection algorithm based on sparse variational autoencoder using spike and slab prior |
| topic | anomaly detection variational autoencoder spike and slab distribution deep support vector network |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2022238/ |
| work_keys_str_mv | AT huahuachen researchonanomalydetectionalgorithmbasedonsparsevariationalautoencoderusingspikeandslabprior AT zhechen researchonanomalydetectionalgorithmbasedonsparsevariationalautoencoderusingspikeandslabprior |