Visualization of Iris Data Using Principal Component Analysis and Kernel Principal Component Analysis

  • Ismail Djakaria Mathematics Department, Gorontalo State University, Gorontalo
  • suryo Guritno Mathematics Department, Gadjah Mada University, Yogyakarta
  • Sri Haryatmi Kartiko Mathematics Department, Gadjah Mada University, Yogyakarta

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: 30 nov. 2024.
Section
General

Keywords

KPCA; non-linear; non-separable; Iris flower dataset