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)

Jurnal Ilmu Dasar
Vol 11 No 1 (2010)

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.