Visualization of classified data with kernel principal component analysis

Toni Bakhtiar

No cbfxu, INA-Rxiv from Center for Open Science

Abstract: Kernel Principal Component Analysis (Kernel PCA) is a generalization of the ordinary PCA which allows mapping the original data into a high-dimensional feature space. The mapping is expected to address the issues of nonlinearity among variables and separation among classes in the original data space. The key problem in the use of kernel PCA is the parameter estimation used in kernel functions that so far has not had quite obvious guidance, where the parameter selection mainly depends on the objectivity of the research. This study exploited the use of Gaussian kernel function and focused on the ability of kernel PCA in visualizing the separation of the classified data. Assessments were undertaken based on misclassification obtained by Fisher Discriminant Linear Analysis of the first two principal components. This study results suggest for the visualization of kernel PCA by selecting the parameter in the interval between the closest and the furthest distances among the objects of original data is better than that of ordinary PCA.

Date: 2018-01-14
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://osf.io/download/5a5b6a8cff20fa000de925d9/

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:osf:inarxi:cbfxu

DOI: 10.31219/osf.io/cbfxu

Access Statistics for this paper

More papers in INA-Rxiv from Center for Open Science
Bibliographic data for series maintained by OSF ().