DimensionReduction

DimensionReduction[{example1,example2,}]

generates a DimensionReducerFunction[] that projects from the space defined by the examplei to a lower-dimensional approximating manifold.

DimensionReduction[examples,n]

generates a DimensionReducerFunction[] for an n-dimensional approximating manifold.

DimensionReduction[examples,n,props]

generates the specified properties of the dimensionality reduction.

Details and Options

  • DimensionReduction can be used on many types of data, including numerical, textual, sounds, and images, as well as combinations of these.
  • DimensionReduction[examples] yields a DimensionReducerFunction[] that can be applied to data to perform dimension reduction.
  • Each examplei can be a single data element, a list of data elements, an association of data elements, or a Dataset object.
  • DimensionReduction[examples] automatically chooses an appropriate dimension for the target approximating manifold.
  • DimensionReduction[examples] is equivalent to DimensionReduction[examples,Automatic].
  • In DimensionReduction[,props], props can be a single property or a list of properties. Possible properties include:
  • "ReducerFunction"DimensionReducerFunction[] (default)
    "ReducedVectors"vectors obtained by reducing the examplei
    "ReconstructedData"reconstruction of examples after reduction and inversion
    "ImputedData"missing values in examples replaced by imputed values
  • The following options can be given:
  • FeatureExtractorIdentityhow to extract features from which to learn
    FeatureNamesAutomaticnames to assign to elements of the examplei
    FeatureTypesAutomaticfeature types to assume for elements of the examplei
    MethodAutomaticwhich reduction algorithm to use
    PerformanceGoalAutomaticaspect of performance to optimize
    RandomSeeding1234what seeding of pseudorandom generators should be done internally
  • Possible settings for PerformanceGoal include:
  • "Memory"minimize the storage requirements of the reducer function
    "Quality"maximize reduction quality
    "Speed"maximize reduction speed
    "TrainingSpeed"minimize the time spent producing the reducer
  • PerformanceGoal{goal1,goal2,} will automatically combine goal1, goal2, etc.
  • Possible settings for Method include:
  • Automaticautomatically chosen method
    "LatentSemanticAnalysis"latent semantic analysis method
    "Linear"automatically choose the best linear method
    "LowRankMatrixFactorization"use a low-rank matrix factorization algorithm
    "PrincipalComponentsAnalysis"principal components analysis method
    "TSNE"t-distributed stochastic neighbor embedding algorithm
    "AutoEncoder"use a trainable autoencoder
  • For Method -> "TSNE", The following suboptions are supported:
  • "Perplexity"Automaticperplexity value to be used
    "LinearPrereduction"Falsewhether to perform a light linear pre-reduction before running the t-SNE algorithm
  • Possible settings for RandomSeeding include:
  • Automaticautomatically reseed every time the function is called
    Inheriteduse externally seeded random numbers
    seeduse an explicit integer or strings as a seed

Examples

open allclose all

Basic Examples  (3)

Generate a dimension reducer from a list of vectors:

In[1]:=
Click for copyable input
Out[1]=

Use this reducer on a new vector:

In[2]:=
Click for copyable input
Out[2]=

Use this reducer on a list of new vectors:

In[3]:=
Click for copyable input
Out[3]=

Create a reducer with a specified target dimension of 1:

In[1]:=
Click for copyable input
In[2]:=
Click for copyable input
Out[2]=

Apply the reducer to the vectors used to generate the reducer:

In[3]:=
Click for copyable input
Out[3]=

Obtain both the reducer and the reduced vectors in one step:

In[4]:=
Click for copyable input
Out[4]=

Train a dimension reducer on a mixed-type dataset:

In[1]:=
Click for copyable input
Out[1]=

Reduce the dimension of a new example:

In[2]:=
Click for copyable input
Out[2]=

Scope  (7)

Options  (6)

Applications  (4)

See Also

DimensionReduce  DimensionReducerFunction  FeatureExtraction  ClusterClassify  Classify  Predict

Introduced in 2015
(10.1)
| Updated in 2017
(11.2)