Author

Ismail Djakaria

Subject

- Sains

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.

Publisher

Universitas Jember

Contributor

-

Publish

2010

Material Type

ARTIKEL

Right

Jurnal ILMU DASAR Vol. 11 No. 1, Januari 2010 : 31 – 38