Ellipsoidal Subspace Support Vector Data Description
In this paper, we propose a novel method for transforming data into a low-dimensional space optimized for one-class classification. The proposed method iteratively transforms data into a new subspace optimized for ellipsoidal encapsulation of target class data. We provide both linear and non-linear...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9133428/ |
| _version_ | 1818663865535168512 |
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| author | Fahad Sohrab Jenni Raitoharju Alexandros Iosifidis Moncef Gabbouj |
| author_facet | Fahad Sohrab Jenni Raitoharju Alexandros Iosifidis Moncef Gabbouj |
| author_sort | Fahad Sohrab |
| collection | DOAJ |
| description | In this paper, we propose a novel method for transforming data into a low-dimensional space optimized for one-class classification. The proposed method iteratively transforms data into a new subspace optimized for ellipsoidal encapsulation of target class data. We provide both linear and non-linear formulations for the proposed method. The method takes into account the covariance of the data in the subspace; hence, it yields a more generalized solution as compared to the data description in the subspace by hyperspherical encapsulation of target class data. We propose different regularization terms expressing the class variance in the projected space. We compare the results with classic and recently proposed one-class classification methods and achieve competing results and show clear improvement compared to the other support vector based methods. The proposed method is also noticed to converge much faster than recently proposed Subspace Support Vector Data Description. |
| first_indexed | 2024-12-17T05:23:38Z |
| format | Article |
| id | doaj.art-3a28fb3e79cf4475a8d591d305f57639 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T05:23:38Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-3a28fb3e79cf4475a8d591d305f576392022-12-21T22:01:56ZengIEEEIEEE Access2169-35362020-01-01812201312202510.1109/ACCESS.2020.30071239133428Ellipsoidal Subspace Support Vector Data DescriptionFahad Sohrab0https://orcid.org/0000-0002-8080-4011Jenni Raitoharju1https://orcid.org/0000-0003-4631-9298Alexandros Iosifidis2Moncef Gabbouj3Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, FinlandFinnish Environment Institute, Jyväskylä, FinlandDepartment of Engineering, Aarhus University, Aarhus, DenmarkFaculty of Information Technology and Communication Sciences, Tampere University, Tampere, FinlandIn this paper, we propose a novel method for transforming data into a low-dimensional space optimized for one-class classification. The proposed method iteratively transforms data into a new subspace optimized for ellipsoidal encapsulation of target class data. We provide both linear and non-linear formulations for the proposed method. The method takes into account the covariance of the data in the subspace; hence, it yields a more generalized solution as compared to the data description in the subspace by hyperspherical encapsulation of target class data. We propose different regularization terms expressing the class variance in the projected space. We compare the results with classic and recently proposed one-class classification methods and achieve competing results and show clear improvement compared to the other support vector based methods. The proposed method is also noticed to converge much faster than recently proposed Subspace Support Vector Data Description.https://ieeexplore.ieee.org/document/9133428/Anomaly detectionellipsoidal data descriptionmachine learningone-class classificationsubspace learning |
| spellingShingle | Fahad Sohrab Jenni Raitoharju Alexandros Iosifidis Moncef Gabbouj Ellipsoidal Subspace Support Vector Data Description IEEE Access Anomaly detection ellipsoidal data description machine learning one-class classification subspace learning |
| title | Ellipsoidal Subspace Support Vector Data Description |
| title_full | Ellipsoidal Subspace Support Vector Data Description |
| title_fullStr | Ellipsoidal Subspace Support Vector Data Description |
| title_full_unstemmed | Ellipsoidal Subspace Support Vector Data Description |
| title_short | Ellipsoidal Subspace Support Vector Data Description |
| title_sort | ellipsoidal subspace support vector data description |
| topic | Anomaly detection ellipsoidal data description machine learning one-class classification subspace learning |
| url | https://ieeexplore.ieee.org/document/9133428/ |
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