Swiss Roll reduction with LLE

An illustration of Swiss Roll reduction with locally linear embedding

Original data, Projected data


Computing LLE embedding
Done. Reconstruction error: 1.36695e-08

# Author: Fabian Pedregosa -- <>
# License: BSD 3 clause (C) INRIA 2011


import matplotlib.pyplot as plt

# This import is needed to modify the way figure behaves
from mpl_toolkits.mplot3d import Axes3D

# Locally linear embedding of the swiss roll

from sklearn import manifold, datasets
X, color = datasets.make_swiss_roll(n_samples=1500)

print("Computing LLE embedding")
X_r, err = manifold.locally_linear_embedding(X, n_neighbors=12,
print("Done. Reconstruction error: %g" % err)

# Plot result

fig = plt.figure()

ax = fig.add_subplot(211, projection='3d')
ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=color,

ax.set_title("Original data")
ax = fig.add_subplot(212)
ax.scatter(X_r[:, 0], X_r[:, 1], c=color,
plt.xticks([]), plt.yticks([])
plt.title('Projected data')

Total running time of the script: ( 0 minutes 0.288 seconds)

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