On feature selection with principal component analysis for one-class SVM

• Main Author: Lian, Heng
• Other Authors: School of Physical and Mathematical Sciences
• Format: Article
• Language: English
• Published: 2013
• Subjects: DRNTU::Science::Mathematics
• 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
• Institution: Nanyang Technological University

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...