Ellipsoidal support vector data description
© 2016 The Natural Computing Applications Forum This paper presents a data domain description formed by the minimum volume covering ellipsoid around a dataset, called “ellipsoidal support vector data description (eSVDD).” The method is analogous to support vector data description (SVDD), but instead...
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th-cmuir.6653943832-418682017-09-28T04:23:54Z Ellipsoidal support vector data description Teeyapan K. Theera-Umpon N. Auephanwiriyakul S. © 2016 The Natural Computing Applications Forum This paper presents a data domain description formed by the minimum volume covering ellipsoid around a dataset, called “ellipsoidal support vector data description (eSVDD).” The method is analogous to support vector data description (SVDD), but instead, with an ellipsoidal domain description allowing tighter space around the data. In eSVDD, a hyperellipsoid extends its ability to describe more complex data patterns by kernel methods. This is explicitly achieved by defining an “empirical feature map” to project the images of given samples to a higher-dimensional space. We compare the performance of the kernelized ellipsoid in one-class classification with SVDD using standard datasets. 2017-09-28T04:23:54Z 2017-09-28T04:23:54Z 2016-05-24 Journal 09410643 2-s2.0-84976607976 10.1007/s00521-016-2343-3 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84976607976&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/41868 |
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© 2016 The Natural Computing Applications Forum This paper presents a data domain description formed by the minimum volume covering ellipsoid around a dataset, called “ellipsoidal support vector data description (eSVDD).” The method is analogous to support vector data description (SVDD), but instead, with an ellipsoidal domain description allowing tighter space around the data. In eSVDD, a hyperellipsoid extends its ability to describe more complex data patterns by kernel methods. This is explicitly achieved by defining an “empirical feature map” to project the images of given samples to a higher-dimensional space. We compare the performance of the kernelized ellipsoid in one-class classification with SVDD using standard datasets. |
| format |
Journal |
| author |
Teeyapan K. Theera-Umpon N. Auephanwiriyakul S. |
| spellingShingle |
Teeyapan K. Theera-Umpon N. Auephanwiriyakul S. Ellipsoidal support vector data description |
| author_facet |
Teeyapan K. Theera-Umpon N. Auephanwiriyakul S. |
| author_sort |
Teeyapan K. |
| title |
Ellipsoidal support vector data description |
| title_short |
Ellipsoidal support vector data description |
| title_full |
Ellipsoidal support vector data description |
| title_fullStr |
Ellipsoidal support vector data description |
| title_full_unstemmed |
Ellipsoidal support vector data description |
| title_sort |
ellipsoidal support vector data description |
| publishDate |
2017 |
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https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84976607976&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/41868 |
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