from sklearn.datasets import make_swiss_roll
from sklearn.manifold import LocallyLinearEmbedding
from sklearn.decomposition import KernelPCA

X_swiss, t = make_swiss_roll(n_samples=1500, noise=0.3, random_state=2)

lle = LocallyLinearEmbedding(n_components=2, n_neighbors=15)
X_pca_swiss_lle = lle.fit_transform(X_swiss)

pca_swiss = KernelPCA(n_components=2, kernel='sigmoid', gamma=1e-3, coef0=1, fit_inverse_transform=True)
X_pca_swiss_pca = pca_swiss.fit_transform(X_swiss)