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

Authors

  • 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

Keywords:

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

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.

 

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Published

2010-01-03

Issue

Section

General