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:
FeatureExtractor Identity how to extract features from which to learn FeatureNames Automatic names to assign to elements of the examplei FeatureTypes Automatic feature types to assume for elements of the examplei Method Automatic which reduction algorithm to use PerformanceGoal Automatic aspect of performance to optimize RandomSeeding 1234 what seeding of pseudorandom generators should be done internally
- Possible settings for PerformanceGoal include:
"Quality" maximize reduction quality "Speed" maximize reduction speed
- Possible settings for Method include:
Automatic automatically 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" Automatic perplexity value to be used "LinearPrereduction" False whether to perform a light linear pre-reduction before running the t-SNE algorithm
- Possible settings for RandomSeeding include:
Automatic automatically reseed every time the function is called Inherited use externally seeded random numbers seed use an explicit integer or strings as a seed
Examplesopen allclose all
Introduced in 2015
(10.1)| Updated in 2017