Basic Examples (1) 

Reduce the dimension of some vectors:

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Scope (2) 

Create and visualize random 3D vectors:

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Visualize this dataset reduced to two dimensions:

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MultidimensionalScaling will reduce to any dimension that is no larger than the input dimension. Here we create data in ten dimensional space, and visualize in three dimensions:

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Properties and Relations (8) 

As is done in the reference page for DimensionReduce, load the Fisher iris dataset from ExampleData:

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Reduce the dimension of the features:

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Group the examples by their species:

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Visualize the reduced dataset:

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Now show some DimensionReduce methods for this same dataset. First we use the "PrincipalComponentsAnalysis" method:

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Use the "TSNE" method:

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Visualize with the "LatentSemanticAnalysis" method:

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The "LatentSemanticAnalysis" method can be attained directly using SingularValueDecomposition:

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Neat Examples (6) 

Illustrate multidimensional scaling on textual data using several popular literature texts from ExampleData:

Break each text into chunks of equal string length:

Find the most common words across all texts:

Create common word frequency vectors for each chunk:

Weight the frequency vectors using the log-entropy method:

Show the result of multidimensional scaling in two dimensions, grouping text chunks by position of title in the list of text names:

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