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doc/modules/manifold.rst

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@@ -359,7 +359,7 @@ tangent spaces to learn the embedding. LTSA can be performed with function
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:target: ../auto_examples/manifold/plot_lle_digits.html
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:align: center
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:scale: 50
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Complexity
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----------
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Multi-dimensional Scaling (MDS)
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===============================
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Multidimensional scaling (:class:`MDS`) seeks a low-dimensional
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`Multidimensional scaling <http://en.wikipedia.org/wiki/Multidimensional_scaling>`_
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(:class:`MDS`) seeks a low-dimensional
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representation of the data in which the distances respect well the
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distances in the original high-dimensional space.
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examples/manifold/plot_manifold_sphere.py

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For a similiar example, where the methods are applied to the
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S-curve dataset, see :ref:`example_manifold_plot_compare_methods.py`
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Note that the purpose of the MDS is to find a low-dimensional
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representation of the data (here 2D) in which the distances respect well
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the distances in the original high-dimensional space, unlike other
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manifold-learning algorithms, it does not seeks an isotropic
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representation of the data in the low-dimensional space.
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Note that the purpose of the :ref:`MDS <multidimensional_scaling>` is
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to find a low-dimensional representation of the data (here 2D) in
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which the distances respect well the distances in the original
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high-dimensional space, unlike other manifold-learning algorithms,
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it does not seeks an isotropic representation of the data in
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the low-dimensional space.
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"""
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# Author: Jaques Grobler <jaques.grobler@inria.fr>

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