Locally linear embbeding Mapper.
This mapper performs dimensionality reduction. It wraps two algorithms provided by the Modular Data Processing (MDP) framework.
Locally linear embedding (LLE) approximates the input data with a low-dimensional surface and reduces its dimensionality by learning a mapping to the surface.
This wrapper class provides access to two different LLE algorithms (i.e. the corresponding MDP processing nodes). 1) An algorithm outlined in An Introduction to Locally Linear Embedding by L. Saul and S. Roweis, using improvements suggested in Locally Linear Embedding for Classification by D. deRidder and R.pl.W. Duin (aka LLENode) and 2) Hessian Locally Linear Embedding analysis based on algorithm outlined in Hessian Eigenmaps: new locally linear embedding techniques for high-dimensional data by C. Grimes and D. Donoho, 2003.
For more information see the MDP website at http://mdp-toolkit.sourceforge.net
Notes
This mapper only provides forward-mapping functionality – no reverse mapping is available.
Available conditional attributes:
(Conditional attributes enabled by default suffixed with +)
Parameters : | k : int
algorithm : {‘LLE’, ‘HLLE’}
nodeargs : None or dict
enable_ca : None or list of str
disable_ca : None or list of str
node : mdp.Node instance
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