.. _example_manifold_plot_lle_digits.py: ============================================================================= Manifold learning on handwritten digits: Locally Linear Embedding, Isomap... ============================================================================= An illustration of various embeddings on the digits dataset. The RandomTreesEmbedding, from the :mod:`sklearn.ensemble` module, is not technically a manifold embedding method, as it learn a high-dimensional representation on which we apply a dimensionality reduction method. However, it is often useful to cast a dataset into a representation in which the classes are linearly-separable. t-SNE will be initialized with the embedding that is generated by PCA in this example, which is not the default setting. It ensures global stability of the embedding, i.e., the embedding does not depend on random initialization. .. rst-class:: horizontal * .. image:: images/plot_lle_digits_001.png :scale: 47 * .. image:: images/plot_lle_digits_002.png :scale: 47 * .. image:: images/plot_lle_digits_003.png :scale: 47 * .. image:: images/plot_lle_digits_004.png :scale: 47 * .. image:: images/plot_lle_digits_005.png :scale: 47 * .. image:: images/plot_lle_digits_006.png :scale: 47 * .. image:: images/plot_lle_digits_007.png :scale: 47 * .. image:: images/plot_lle_digits_008.png :scale: 47 * .. image:: images/plot_lle_digits_009.png :scale: 47 * .. image:: images/plot_lle_digits_010.png :scale: 47 * .. image:: images/plot_lle_digits_011.png :scale: 47 * .. image:: images/plot_lle_digits_012.png :scale: 47 * .. image:: images/plot_lle_digits_013.png :scale: 47 **Script output**:: Computing random projection Computing PCA projection Computing LDA projection Computing Isomap embedding Done. Computing LLE embedding Done. Reconstruction error: 1.63539e-06 Computing modified LLE embedding Done. Reconstruction error: 0.360702 Computing Hessian LLE embedding Done. Reconstruction error: 0.212759 Computing LTSA embedding Done. Reconstruction error: 0.212806 Computing MDS embedding Done. Stress: 139486576.843200 Computing Totally Random Trees embedding Computing Spectral embedding Computing t-SNE embedding **Python source code:** :download:`plot_lle_digits.py ` .. literalinclude:: plot_lle_digits.py :lines: 19- **Total running time of the example:** 33.82 seconds ( 0 minutes 33.82 seconds)