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Machine Learning for Beginners in Python: Dimensionality Reduction With Kernel PCA

By on Monday, May 24, 2021

Dimensionality Reduction With Kernel PCA

Preliminaries

/* Load libraries */
from sklearn.decomposition import PCA, KernelPCA
from sklearn.datasets import make_circles

Create Linearly Inseparable Data

/* Create linearly inseparable data */
X, _ = make_circles(n_samples=1000, random_state=1, noise=0.1, factor=0.1)

Conduct Kernel PCA

/* Apply kernal PCA with radius basis function (RBF) kernel */
kpca = KernelPCA(kernel="rbf", gamma=15, n_components=1)
X_kpca = kpca.fit_transform(X)

View Results


print('Original number of features:', X.shape[1])
print('Reduced number of features:', X_kpca.shape[1])

Original number of features: 2
Reduced number of features: 1

 

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