Visualization of Iris Data Using Principal Component Analysis and Kernel Principal Component Analysis
Abstract
Principal component analysis (PCA) is a method used to reduce dimentionality of the dataset. However, the use of PCA failed to carry out the problem of non-linear and non-separable data. To overcome this problem such data is more appropriate to use PCA method with the kernel function, which is known as the kernel PCA (KPCA). In this paper, Iris dataset visualized with PCA and KPCA, that contains are the length and the width of sepal and petal.
Published
2010-01-03
How to Cite
DJAKARIA, Ismail; GURITNO, suryo; KARTIKO, Sri Haryatmi.
Visualization of Iris Data Using Principal Component Analysis and Kernel Principal Component Analysis.
Jurnal ILMU DASAR, [S.l.], v. 11, n. 1, p. 31-38, jan. 2010.
ISSN 2442-5613.
Available at: <https://jurnal.unej.ac.id/index.php/JID/article/view/104>. Date accessed: 13 nov. 2024.
Issue
Section
General
Keywords
KPCA; non-linear; non-separable; Iris flower dataset