On feature selection with principal component analysis for one-class SVM
In this short note, we demonstrate the use of principal components analysis (PCA) for one-class support vector machine (one-class SVM) as a dimension reduction tool. However, unlike almost all other usage of PCA which extracts the eigenvectors associated with top eigenvalues as the projection direct...
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| Format: | Article |
| Language: | English |
| Published: |
2013
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| Online Access: | https://hdl.handle.net/10356/105603 http://hdl.handle.net/10220/17154 http://dx.doi.org/10.1016/j.patrec.2012.01.019 |
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| Institution: | Nanyang Technological University |
| Language: | English |