| dc.contributor.author | WU Huinan, XING Hongjie, LI Gang | |
| dc.contributor.other | 1 Hebei Key Laboratory of Machine Learning and Computational Intelligence,College of Mathematics and Information Science,Hebei University,Baoding,Hebei 071002,China ;2 Department of Computer,North China Electric Power University,Baoding,Hebei 071003,China ;3 Engineering Research Center of Intelligent Computing for Complex Energy Systems,Baoding,Hebei 071003,China | |
| dc.date.accessioned | 2025-08-27T02:40:24Z | |
| dc.date.accessioned | 2025-10-08T09:26:32Z | |
| dc.date.available | 2025-10-08T09:26:32Z | |
| dc.date.issued | 01-06-2024 | |
| dc.identifier.uri | http://digilib.fisipol.ugm.ac.id/repo/handle/15717717/40407 | |
| dc.description.abstract | With the continuous increase of data dimension and scale,anomaly detection methods based on deep learning have achieved excellent detection performance,among which deep support vector data description(Deep SVDD) has been widely used.However,it is necessary to impose constraints on various parameters of the mapping network in Deep SVDD to alleviate the hypersphere collapse problem.In order to further improve the feature learning ability of the mapping network in Deep SVDD and solve the hypersphere collapse problem,deep multiple-sphere support vector data description based on variational autoencoder with mixture-of-gaussians prior(DMSVDD-VAE-MoG) is proposed.First,the network parameters and multiple hypersphere centers are initialized by pre-training.Second,the latent features of the training data are obtained by mapping network.The VAE loss,the average radius of multiple hyperspheres together with the average distance between the latent features and their corres-ponding hypersphere centers are jointly optimized to obtain the optimal network connection weights and multiple minimum hyperspheres.In comparison with the other eight related methods,the experimental results show that the proposed DMSVDD-VAE-MoG achieves better detection performance upon MNIST,Fashion-MNIST and CIFAR-10. | |
| dc.language.iso | ZH | |
| dc.publisher | Editorial office of Computer Science | |
| dc.subject.lcc | Computer software | |
| dc.title | Deep Multiple-sphere Support Vector Data Description Based on Variational Autoencoder with Mixture-of-Gaussians Prior | |
| dc.type | Article | |
| dc.description.keywords | deep support vector data description|mixture-of-gaussians prior|variational autoencoder|anomaly detection|hypersphere collapse | |
| dc.description.pages | 135-143 | |
| dc.description.doi | 10.11896/jsjkx.230300194 | |
| dc.title.journal | Jisuanji kexue | |
| dc.identifier.oai | oai:doaj.org/journal:934e19df82ea4dca884b45570dd705f5 | |
| dc.journal.info | Volume 51, Issue 6 | |
