High-dimensional outlier detection based on deep belief network and linear one-class SVM
Aiming at the difficulties in high-dimensional outlier detection at present,an algorithm of high-dimensional outlier detection based on deep belief network and linear one-class SVM was proposed.The algorithm firstly used the deep belief network which had a good performance in the feature extraction...
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2018-01-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2018006/ |
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| _version_ | 1841530309917540352 |
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| author | Haoqi LI Na YING Chunsheng GUO Jinhua WANG |
| author_facet | Haoqi LI Na YING Chunsheng GUO Jinhua WANG |
| author_sort | Haoqi LI |
| collection | DOAJ |
| description | Aiming at the difficulties in high-dimensional outlier detection at present,an algorithm of high-dimensional outlier detection based on deep belief network and linear one-class SVM was proposed.The algorithm firstly used the deep belief network which had a good performance in the feature extraction to realize the dimensionality reduction of high-dimensional data,and then the outlier detection was achieved based on a one-class SVM with the linear kernel function.High-dimensional data sets in UCI machine learning repository were selected to experiment,result shows that the algorithm has obvious advantages in detection accuracy and computational complexity.Compared with the PCA-SVDD algorithm,the detection accuracy is improved by 4.65%.Compared with the automatic encoder algorithm,its training time and testing time decrease significantly. |
| format | Article |
| id | doaj-art-a155ef710de34c93bd6d3c6ff0f528c6 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2018-01-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-a155ef710de34c93bd6d3c6ff0f528c62025-01-15T03:05:20ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012018-01-0134344259597417High-dimensional outlier detection based on deep belief network and linear one-class SVMHaoqi LINa YINGChunsheng GUOJinhua WANGAiming at the difficulties in high-dimensional outlier detection at present,an algorithm of high-dimensional outlier detection based on deep belief network and linear one-class SVM was proposed.The algorithm firstly used the deep belief network which had a good performance in the feature extraction to realize the dimensionality reduction of high-dimensional data,and then the outlier detection was achieved based on a one-class SVM with the linear kernel function.High-dimensional data sets in UCI machine learning repository were selected to experiment,result shows that the algorithm has obvious advantages in detection accuracy and computational complexity.Compared with the PCA-SVDD algorithm,the detection accuracy is improved by 4.65%.Compared with the automatic encoder algorithm,its training time and testing time decrease significantly.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2018006/outlier detectionhigh-dimensional datadeep belief networkone-class SVM |
| spellingShingle | Haoqi LI Na YING Chunsheng GUO Jinhua WANG High-dimensional outlier detection based on deep belief network and linear one-class SVM Dianxin kexue outlier detection high-dimensional data deep belief network one-class SVM |
| title | High-dimensional outlier detection based on deep belief network and linear one-class SVM |
| title_full | High-dimensional outlier detection based on deep belief network and linear one-class SVM |
| title_fullStr | High-dimensional outlier detection based on deep belief network and linear one-class SVM |
| title_full_unstemmed | High-dimensional outlier detection based on deep belief network and linear one-class SVM |
| title_short | High-dimensional outlier detection based on deep belief network and linear one-class SVM |
| title_sort | high dimensional outlier detection based on deep belief network and linear one class svm |
| topic | outlier detection high-dimensional data deep belief network one-class SVM |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2018006/ |
| work_keys_str_mv | AT haoqili highdimensionaloutlierdetectionbasedondeepbeliefnetworkandlinearoneclasssvm AT naying highdimensionaloutlierdetectionbasedondeepbeliefnetworkandlinearoneclasssvm AT chunshengguo highdimensionaloutlierdetectionbasedondeepbeliefnetworkandlinearoneclasssvm AT jinhuawang highdimensionaloutlierdetectionbasedondeepbeliefnetworkandlinearoneclasssvm |