We can now apply this algorithm to the Olivetti faces dataset, instantiating the Scikit-Learn class LocallyLinearEmbedding with n_components=2 and n_neighbors=15:
from sklearn.manifold import LocallyLinearEmbeddinglle = LocallyLinearEmbedding(n_neighbors=15, n_components=2)X_lle = lle.fit_transform(faces['data'])
The result (limited to the first 100 samples) is shown in the following plot:
Locally linear embedding applied to 100 samples drawn from the Olivetti faces dataset
Even if the strategy is different from Isomap, we can determine some coherent clusters. In this case, the similarity is obtained through ...
