Wolfram Language & System 10.3 (2015)|Legacy Documentation

This is documentation for an earlier version of the Wolfram Language.
BUILT-IN WOLFRAM LANGUAGE SYMBOL

DimensionReduce

DimensionReduce[{vec1,vec2,}]
projects the vectors onto an approximating manifold in lower-dimensional space.

DimensionReduce[vecs,n]
projects onto an approximating manifold in n-dimensional space.

DimensionReduce[vecs,n,data]
applies the projection deduced from vecs to the list of vectors data.

DimensionReduce[vecs,n,data,prop]
gives the specified property of the reduction associated with data.

Details and OptionsDetails and Options

• The vectors must be numerical and must all be of the same length.
• DimensionReduce[vecs] automatically chooses an appropriate dimension for the approximating manifold.
• DimensionReduce[vecs] is equivalent to DimensionReduce[vecs,Automatic].
• The vectors in the list data must be the same length as the .
• DimensionReduce only works on numerical vectors all having the same length.
• In DimensionReduce[vecs,n,data,prop], possible properties include:
•  "ReducedVectors" reduction of the vectors given "OriginalVectors" deduced original vectors given reduced vectors "ReconstructedVectors" reconstruction by reduction and inversion "ImputedVectors" missing values replaced by imputed ones
• The following options can be given:
•  Method Automatic which reduction algorithm to use PerformanceGoal Automatic aspect of performance to optimize
• Possible settings for PerformanceGoal include:
•  "Quality" maximize reduction quality "Speed" maximize reduction speed
• Possible settings for Method include:
•  Automatic automatically chosen method "PrincipalComponentsAnalysis" principal components analysis method "LatentSemanticAnalysis" latent semantic analysis method "LowRankMatrixFactorization" use a low-rank matrix factorization algorithm

ExamplesExamplesopen allclose all

Basic Examples  (2)Basic Examples  (2)

Reduce the dimension of vectors:

 Out[2]=

Specify that the target dimension should be 1:

 Out[3]=

Create a dimensionality reducer using vectors, and reduce the dimension of other vectors in one step:

 Out[3]=

Perform the reduction, then the inverse operation:

 Out[4]=