Multivariate Pattern Analysis in Python |
Inheritance diagram for mvpa.mappers.lle:
Local Linear Embedding Data mapper.
This is a wrapper class around the corresponding MDP nodes LLE and HLLE (since MDP 2.4).
Bases: mvpa.mappers.base.Mapper
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.P.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.
Note
This mapper only provides forward-mapping functionality – no reverse mapping is available.
See also
Parameters: |
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Map data from the IN dataspace into OUT space.
Returns the size of the entity in input space
Returns the size of the entity in output space
Provide access to the underlying MDP processing node.
With some care.
Reverse map data from OUT space into the IN space.
Train the mapper.