projects the examples examplei to a lower-dimensional approximating manifold.


projects onto an approximating manifold in n-dimensional space.

Details and Options

  • DimensionReduce can be used on many types of data, including numerical, textual, sounds, and images, as well as combinations of these.
  • Each examplei can be a single data element, a list of data elements, an association of data elements, or a Dataset object.
  • DimensionReduce[examples] automatically chooses an appropriate dimension for the approximating manifold.
  • DimensionReduce[examples] is equivalent to DimensionReduce[examples,Automatic].
  • 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
  • Possible settings for PerformanceGoal include:
  • "Quality"maximize reduction quality
    "Speed"maximize reduction speed
  • 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


open allclose all

Basic Examples  (2)

Reduce the dimension of vectors:

Click for copyable input
Click for copyable input

Specify that the target dimension should be 1:

Click for copyable input

Reduce the dimension of a mixed-type dataset:

Click for copyable input

Scope  (6)

Options  (4)

Applications  (1)

See Also

DimensionReduction  DimensionReducerFunction  FeatureExtract  PrincipalComponents  ClusterClassify  Classify  Predict  ListSurfacePlot3D

Introduced in 2015
| Updated in 2017