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All Journal Jurnal Ilmu Dasar
suryo Guritno
Mathematics Department, Gadjah Mada University, Yogyakarta
Control & Systems Engineering Mathematics

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Visualization of Iris Data Using Principal Component Analysis and Kernel Principal Component Analysis Ismail Djakaria; suryo Guritno; Sri Haryatmi Kartiko
Jurnal ILMU DASAR Vol 11 No 1 (2010)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Jember

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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. 
Co-Authors Ismail Djakaria Sri Haryatmi Kartiko