.. _example_manifold_plot_compare_methods.py: ========================================= Comparison of Manifold Learning methods ========================================= An illustration of dimensionality reduction on the S-curve dataset with various manifold learning methods. For a discussion and comparison of these algorithms, see the :ref:`manifold module page ` For a similar example, where the methods are applied to a sphere dataset, see :ref:`example_manifold_plot_manifold_sphere.py` Note that the purpose of the MDS is to find a low-dimensional representation of the data (here 2D) in which the distances respect well the distances in the original high-dimensional space, unlike other manifold-learning algorithms, it does not seeks an isotropic representation of the data in the low-dimensional space. .. image:: images/plot_compare_methods_001.png :align: center **Script output**:: standard: 0.12 sec ltsa: 0.26 sec hessian: 0.32 sec modified: 0.25 sec Isomap: 0.67 sec MDS: 3.1 sec SpectralEmbedding: 0.17 sec t-SNE: 20 sec **Python source code:** :download:`plot_compare_methods.py ` .. literalinclude:: plot_compare_methods.py :lines: 21- **Total running time of the example:** 25.51 seconds ( 0 minutes 25.51 seconds)