Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/105603
Title: | On feature selection with principal component analysis for one-class SVM | Authors: | Lian, Heng | Keywords: | DRNTU::Science::Mathematics | Issue Date: | 2012 | Source: | Lian, H. (2012). On feature selection with principal component analysis for one-class SVM. Pattern recognition letters, 33(9), 1027-1031. | Series/Report no.: | Pattern recognition letters | Abstract: | 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 directions, here it is the eigenvectors associated with small eigenvalues that are of interests, and in particular the null of the eigenspace, since the null space in fact characterizes the common features of the training samples. Image retrieval examples are used to illustrate the effectiveness of dimension reduction. | URI: | https://hdl.handle.net/10356/105603 http://hdl.handle.net/10220/17154 |
ISSN: | 0167-8655 | DOI: | 10.1016/j.patrec.2012.01.019 | Schools: | School of Physical and Mathematical Sciences | Fulltext Permission: | none | Fulltext Availability: | No Fulltext |
Appears in Collections: | SPMS Journal Articles |
SCOPUSTM
Citations
10
34
Updated on Mar 27, 2024
Web of ScienceTM
Citations
10
26
Updated on Oct 25, 2023
Page view(s) 50
496
Updated on Mar 29, 2024
Google ScholarTM
Check
Altmetric
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.