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DimensionReduce
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DimensionReduce

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DimensionReduce[{example1,example2,…}]

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

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DimensionReduce[examples,n]

projects onto an approximating manifold in n-dimensional space.

Details and Options

Examples

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Basic Examples  (2)Summary of the most common use cases

Reduce the dimension of vectors:

In[1]:=1
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https://wolfram.com/xid/0d6gsbywf2-t51o2a
In[2]:=2
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https://wolfram.com/xid/0d6gsbywf2-9e53pz
Out[2]=2

Specify that the target dimension should be 1:

In[3]:=3
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https://wolfram.com/xid/0d6gsbywf2-3nhpq8
Out[3]=3

Reduce the dimension of a mixed-type dataset:

In[1]:=1
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https://wolfram.com/xid/0d6gsbywf2-zt6cqe
Out[1]=1

Scope  (6)Survey of the scope of standard use cases

Create and visualize random 3D vectors:

In[1]:=1
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https://wolfram.com/xid/0d6gsbywf2-eg1rcs
In[2]:=2
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https://wolfram.com/xid/0d6gsbywf2-oanlck
Out[2]=2

Visualize this dataset reduced to two dimensions:

In[3]:=3
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https://wolfram.com/xid/0d6gsbywf2-h7iufj
Out[3]=3

Reduce the dimension of a dataset of images:

In[1]:=1
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https://wolfram.com/xid/0d6gsbywf2-kzhnwu
Out[1]=1

Reduce the dimension of textual data:

In[1]:=1
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https://wolfram.com/xid/0d6gsbywf2-mfh7fb
Out[1]=1

Reduce the dimension of a list of DateObject:

In[1]:=1
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https://wolfram.com/xid/0d6gsbywf2-1bvc2c
Out[1]=1

Reduce the dimension of a mixed-type dataset:

In[1]:=1
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https://wolfram.com/xid/0d6gsbywf2-ksjcxt
Out[1]=1

Reduce the dimension of a list of associations:

In[1]:=1
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https://wolfram.com/xid/0d6gsbywf2-myka0y
Out[1]=1

Options  (6)Common values & functionality for each option

FeatureExtractor  (1)

Reduce the dimension of texts preprocessed by custom functions and an extractor method:

In[1]:=1
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https://wolfram.com/xid/0d6gsbywf2-y7aarg
Out[1]=1

FeatureTypes  (1)

Reduce the dimension of a simple dataset:

In[1]:=1
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https://wolfram.com/xid/0d6gsbywf2-9einrr
Out[1]=1

The first feature has been interpreted as numerical. Use FeatureTypes to enforce the interpretation of the first feature as nominal:

In[2]:=2
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https://wolfram.com/xid/0d6gsbywf2-noogrg
Out[2]=2

Method  (2)

Reduce the dimension of the Fisher iris dataset using the t-SNE method:

In[1]:=1
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https://wolfram.com/xid/0d6gsbywf2-8pmijs
In[2]:=2
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https://wolfram.com/xid/0d6gsbywf2-zkwqoh

Visualize the reduced dataset:

In[3]:=3
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https://wolfram.com/xid/0d6gsbywf2-dmige5
Out[3]=3

Perform the same operation using a different perplexity value:

In[4]:=4
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https://wolfram.com/xid/0d6gsbywf2-t6jes2
In[5]:=5
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https://wolfram.com/xid/0d6gsbywf2-hc3c1y
Out[5]=5

Reduce the dimension of some images using the auto-encoder method:

In[1]:=1
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https://wolfram.com/xid/0d6gsbywf2-1kk4ik
In[2]:=2
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https://wolfram.com/xid/0d6gsbywf2-57tu7w

Visualize the reduced dataset:

In[3]:=3
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https://wolfram.com/xid/0d6gsbywf2-1heqne
Out[3]=3

PerformanceGoal  (1)

Load the MNIST dataset:

In[1]:=1
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https://wolfram.com/xid/0d6gsbywf2-1f875d

Reduce the dimension of the images data with the setting PerformanceGoal"Quality" and measure the training time:

In[2]:=2
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https://wolfram.com/xid/0d6gsbywf2-phlfj1
Out[2]=2

Perform the same operation using PerformanceGoal"Speed":

In[3]:=3
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https://wolfram.com/xid/0d6gsbywf2-ju2wtv
Out[3]=3

Visualize the results:

In[4]:=4
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https://wolfram.com/xid/0d6gsbywf2-79ai06
Out[4]=4
In[5]:=5
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https://wolfram.com/xid/0d6gsbywf2-j442dg
Out[5]=5

TargetDevice  (1)

Reduce the dimension of vectors using a fully connected "AutoEncoder" on the system's default GPU and look at its AbsoluteTiming:

In[1]:=1
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https://wolfram.com/xid/0d6gsbywf2-nni7sq

Compare the previous timing with the one obtained by using the default CPU computation:

In[2]:=2
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https://wolfram.com/xid/0d6gsbywf2-x7frfj

Applications  (1)Sample problems that can be solved with this function

Dataset Visualization  (1)

Load the Fisher iris dataset from ExampleData:

In[1]:=1
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https://wolfram.com/xid/0d6gsbywf2-i0qojo
In[2]:=2
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https://wolfram.com/xid/0d6gsbywf2-dvaxto
Out[2]=2
In[3]:=3
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https://wolfram.com/xid/0d6gsbywf2-ljl8f4
Out[3]=3

Reduce the dimension of the features:

In[4]:=4
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https://wolfram.com/xid/0d6gsbywf2-hf3wmk

Group the examples by their species:

In[5]:=5
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https://wolfram.com/xid/0d6gsbywf2-u41y3o

Visualize the reduced dataset:

In[6]:=6
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https://wolfram.com/xid/0d6gsbywf2-ck8dgi
Out[6]=6
Wolfram Research (2015), DimensionReduce, Wolfram Language function, https://reference.wolfram.com/language/ref/DimensionReduce.html (updated 2018).
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Wolfram Research (2015), DimensionReduce, Wolfram Language function, https://reference.wolfram.com/language/ref/DimensionReduce.html (updated 2018).

Text

Wolfram Research (2015), DimensionReduce, Wolfram Language function, https://reference.wolfram.com/language/ref/DimensionReduce.html (updated 2018).

✖
Wolfram Research (2015), DimensionReduce, Wolfram Language function, https://reference.wolfram.com/language/ref/DimensionReduce.html (updated 2018).

CMS

Wolfram Language. 2015. "DimensionReduce." Wolfram Language & System Documentation Center. Wolfram Research. Last Modified 2018. https://reference.wolfram.com/language/ref/DimensionReduce.html.

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Wolfram Language. 2015. "DimensionReduce." Wolfram Language & System Documentation Center. Wolfram Research. Last Modified 2018. https://reference.wolfram.com/language/ref/DimensionReduce.html.

APA

Wolfram Language. (2015). DimensionReduce. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/DimensionReduce.html

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Wolfram Language. (2015). DimensionReduce. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/DimensionReduce.html

BibTeX

@misc{reference.wolfram_2025_dimensionreduce, author="Wolfram Research", title="{DimensionReduce}", year="2018", howpublished="\url{https://reference.wolfram.com/language/ref/DimensionReduce.html}", note=[Accessed: 25-March-2025 ]}

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@misc{reference.wolfram_2025_dimensionreduce, author="Wolfram Research", title="{DimensionReduce}", year="2018", howpublished="\url{https://reference.wolfram.com/language/ref/DimensionReduce.html}", note=[Accessed: 25-March-2025 ]}

BibLaTeX

@online{reference.wolfram_2025_dimensionreduce, organization={Wolfram Research}, title={DimensionReduce}, year={2018}, url={https://reference.wolfram.com/language/ref/DimensionReduce.html}, note=[Accessed: 25-March-2025 ]}

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@online{reference.wolfram_2025_dimensionreduce, organization={Wolfram Research}, title={DimensionReduce}, year={2018}, url={https://reference.wolfram.com/language/ref/DimensionReduce.html}, note=[Accessed: 25-March-2025 ]}