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Plot Multivariate Menggunakan Kernel Principal Component Analysis (KPCA) dengan Fungsi Power Kernel

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Kernel PCA merupakan PCA yang diaplikasikan pada input data yang telah ditransformasikan ke feature space. Misalkan F: Rn®F fungsi yang memetakan semua input data xiÎRn, berlaku F(xi)ÎF. Salah satu dari banyak fungsi kernel adalah power kernel. Fungsi power kernel K(xi, xj) = –|| xi – xj ||b dengan 0 < b ≤ 1. Tujuan dari penelitian ini yaitu mempelajari penggunaan Kernel PCA (KPCA) dengan fungsi Power Kernel untuk membantu menyelesaikan masalah plot multivariate nonlinier terutama yang berhubungan dalam pengelompokan. Hasil menunjukkan bahwa Penggunaan KPCA dengan fungsi Power Kernel sangat membantu dalam menyelesaikan masalah plot multivariate yang belum dapat dikelompokan dengan garis pemisah yang linier. Kata kunci : Kernel Principal Component Analysis (KPCA), Plot Multivariate, Power Kernel
Title: Plot Multivariate Menggunakan Kernel Principal Component Analysis (KPCA) dengan Fungsi Power Kernel
Description:
Kernel PCA merupakan PCA yang diaplikasikan pada input data yang telah ditransformasikan ke feature space.
Misalkan F: Rn®F fungsi yang memetakan semua input data xiÎRn, berlaku F(xi)ÎF.
Salah satu dari banyak fungsi kernel adalah power kernel.
Fungsi power kernel K(xi, xj) = –|| xi – xj ||b dengan 0 < b ≤ 1.
Tujuan dari penelitian ini yaitu mempelajari penggunaan Kernel PCA (KPCA) dengan fungsi Power Kernel untuk membantu menyelesaikan masalah plot multivariate nonlinier terutama yang berhubungan dalam pengelompokan.
Hasil menunjukkan bahwa Penggunaan KPCA dengan fungsi Power Kernel sangat membantu dalam menyelesaikan masalah plot multivariate yang belum dapat dikelompokan dengan garis pemisah yang linier.
Kata kunci : Kernel Principal Component Analysis (KPCA), Plot Multivariate, Power Kernel.

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