I've tried the example that is available here
http://scikit-learn.org/stable/auto_examples/manifold/plot_manifold_sphere.html
These are essentially points on a 3D sphere, so the dimension of the embedded
manifold is two.
I've changed the example a little bit to extract the error as well. So instead
of
trans_data = manifold\
.LocallyLinearEmbedding(n_neighbors, 2,
method=method).fit_transform(sphere_data).T
I've done something like
solver = manifold.LocallyLinearEmbedding(n_neighbors, dim_y, method=method)
trans_data = solver.fit_transform(sphere_data).T
error = solver.reconstruction_error_
I would have expected the error to be significant for dim_y=1, since I can't
reproduce with just a single coordinate the results. For dim_y=2, I expected a
significant decrease, and for dim_y=3, I expected to exactly recover the
original result.
What I get is (for standard LLE)
dim_y = 1 : error = 1.62031573333e-07
dim_y = 2 : error = 1.79465538543e-06
dim_y = 3 : error = 7.00280676182e-06
Could anyone explain, why I do not get the expected results?
Furthermore, is there an option to retransform the coordinates from the local
dimension to the global dimension? I'm interested in transforming the original
global samples to local coordinates (this is done via the transform method),
but then I would like to transform samples from coordinates in the embedded
space back into the global space.
Best regards,
Jörg F. Unger
------------------------------------------------------------------------------
What NetFlow Analyzer can do for you? Monitors network bandwidth and traffic
patterns at an interface-level. Reveals which users, apps, and protocols are
consuming the most bandwidth. Provides multi-vendor support for NetFlow,
J-Flow, sFlow and other flows. Make informed decisions using capacity planning
reports. http://pubads.g.doubleclick.net/gampad/clk?id=1444514421&iu=/41014381
_______________________________________________
Scikit-learn-general mailing list
Scikit-learn-general@lists.sourceforge.net
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
