Class LLEMapper

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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.

.. seealso::
  http://mdp-toolkit.sourceforge.net

Instance Methods

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__init__(self, k, algorithm='lle', **kwargs)
x.__init__(...) initializes x; see help(type(x)) for signature

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train(self, ds)
Train the mapper.

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forward(self, data)
Map data from the IN dataspace into OUT space.

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reverse(self, data)
Reverse map data from OUT space into the IN space.

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getInSize(self)
Returns the size of the entity in input space

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getOutSize(self)
Returns the size of the entity in output space

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_accessNode(self)
Provide access to the underlying MDP processing node.

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Inherited from base.Mapper: __call__, __repr__, getInId, getMetric, getNeighbor, getNeighborIn, getNeighbors, isValidInId, isValidOutId, selectOut, setMetric

Inherited from object: __delattr__, __format__, __getattribute__, __hash__, __new__, __reduce__, __reduce_ex__, __setattr__, __sizeof__, __str__, __subclasshook__

Class Variables

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node = property(fget= _accessNode)

Inherited from base.Mapper: metric, nfeatures

Properties

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Inherited from object: __class__

Method Details

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init(self, k, algorithm='lle', **kwargs)
(Constructor)

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x.__init__(...) initializes x; see help(type(x)) for signature

Parameters:

metric - Optional metric

Overrides: object.__init__

Parameters:

k: int: Number of nearest neighbor to be used by the algorithm.
algorithm: 'lle' | 'hlle': Either use the standard LLE algorithm or Hessian Linear Local Embedding (HLLE).
**kwargs:: Additional arguments are passed to the underlying MDP node. Most importantly this is the output_dim argument, that determines the number of dimensions to mapper is using as output space.