Kernel principal component analysis in scikit-learn

For our convenience, scikit-learn implements a kernel PCA class in the sklearn.decomposition
submodule. The usage is similar to the standard PCA class, and we can specify the kernel via the
kernel parameter .

from sklearn.decomposition import KernelPCA
X, y = make_moons(n_samples=100, random_state=123)
scikit_kpca = KernelPCA(n_components=2, kernel='rbf', gamma=15)
X_skernpca = scikit_kpca.fit_transform(X)

Plot the transformed half-moon shape data onto the frst two principal components

plt.scatter(X_skernpca[y == 0, 0], X_skernpca[y == 0, 1],
color='red', marker='^', alpha=0.5)
plt.scatter(X_skernpca[y == 1, 0], X_skernpca[y == 1, 1],
color='blue', marker='o', alpha=0.5)
plt.xlabel('PC1')
plt.ylabel('PC2')
plt.show()

Reference:《Python Machine Learning》