scikit-learn
diff --git a/‎sklearn/manifold/isomap.py
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diff --git a/‎sklearn/manifold/locally_linear.py
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diff --git a/‎sklearn/manifold/mds.py
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diff --git a/‎sklearn/manifold/spectral_embedding_.py
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@@ -80,6 +80,18 @@ class Isomap(BaseEstimator, TransformerMixin):
     dist_matrix_ : array-like, shape (n_samples, n_samples)
         Stores the geodesic distance matrix of training data.
 
+    Examples
+    --------
+    >>> from sklearn.datasets import load_digits
+    >>> from sklearn.manifold import Isomap
+    >>> X, _ = load_digits(return_X_y=True)
+    >>> X.shape
+    (1797, 64)
+    >>> embedding = Isomap(n_components=2)
+    >>> X_transformed = embedding.fit_transform(X[:100])
+    >>> X_transformed.shape
+    (100, 2)
+
     References
     ----------
 
 
@@ -598,6 +598,18 @@ class LocallyLinearEmbedding(BaseEstimator, TransformerMixin):
         Stores nearest neighbors instance, including BallTree or KDtree
         if applicable.
 
+    Examples
+    --------
+    >>> from sklearn.datasets import load_digits
+    >>> from sklearn.manifold import LocallyLinearEmbedding
+    >>> X, _ = load_digits(return_X_y=True)
+    >>> X.shape
+    (1797, 64)
+    >>> embedding = LocallyLinearEmbedding(n_components=2)
+    >>> X_transformed = embedding.fit_transform(X[:100])
+    >>> X_transformed.shape
+    (100, 2)
+
     References
     ----------
 
 
@@ -340,6 +340,17 @@ class MDS(BaseEstimator):
         The final value of the stress (sum of squared distance of the
         disparities and the distances for all constrained points).
 
+    Examples
+    --------
+    >>> from sklearn.datasets import load_digits
+    >>> from sklearn.manifold import MDS
+    >>> X, _ = load_digits(return_X_y=True)
+    >>> X.shape
+    (1797, 64)
+    >>> embedding = MDS(n_components=2)
+    >>> X_transformed = embedding.fit_transform(X[:100])
+    >>> X_transformed.shape
+    (100, 2)
 
     References
     ----------
 
@@ -395,6 +395,18 @@ class SpectralEmbedding(BaseEstimator):
     affinity_matrix_ : array, shape = (n_samples, n_samples)
         Affinity_matrix constructed from samples or precomputed.
 
+    Examples
+    --------
+    >>> from sklearn.datasets import load_digits
+    >>> from sklearn.manifold import SpectralEmbedding
+    >>> X, _ = load_digits(return_X_y=True)
+    >>> X.shape
+    (1797, 64)
+    >>> embedding = SpectralEmbedding(n_components=2)
+    >>> X_transformed = embedding.fit_transform(X[:100])
+    >>> X_transformed.shape
+    (100, 2)
+
     References
     ----------