NDLI: A method for determining the number of features in the kernel space required for preserving classifiability

Content Provider	Springer Nature Link
Author	Yektaii, Mahdi Ahmad, M. Omair Bhattacharya, Prabir
Copyright Year	2014
Abstract	Feature generation techniques that sort the generated features in terms of their importance, such as principal component analysis (PCA), reduce the problem of feature subset selection to only determining the number of features to be retained. For databases with linearly inseparable classes, kernel PCA can be used as the feature generation method instead of the linear PCA. However, determining the number of features in the kernel space that needs to be retained for preserving the classifiability is a difficult problem, since the data vectors are not available in an explicit form in that space. In this paper, we propose a criterion and an algorithm for determining the number of required features in kernel PCA using only the elements of the kernel matrix. In order to show the effectiveness of the proposed criterion, the new algorithm is applied to the USPS handwritten digit, Yale Face and Caltech 101 databases. The proposed algorithm is also investigated for its robustness to noise that corrupts the data samples.
Starting Page	1649
Ending Page	1658
Page Count	10
File Format	PDF
ISSN	18631703
Journal	Signal, Image and Video Processing
Volume Number	9
Issue Number	7
e-ISSN	18631711
Language	English
Publisher	Springer London
Publisher Date	2014-02-12
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Kernel space Principal component analysis Dimensionality reduction Feature subset selection Classifiability Signal, Image and Speech Processing Image Processing and Computer Vision Computer Imaging, Vision, Pattern Recognition and Graphics Multimedia Information Systems
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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