"PrincipalComponentsAnalysis" (Machine Learning Method)
- Method for DimensionReduction and DimensionReduce.
- Maps the data into a lower-dimensional space using the principal components analysis method.
Details & Suboptions
- "PrincipalComponentsAnalysis" is a linear dimensionality reduction method. The method projects input data on a linear lower-dimensional space that preserves the maximum variance in the data.
- The "PrincipalComponentsAnalysis" method works for datasets that have a large number of features and large number of examples; however, the learned manifold can only be linear.
- The following plots show the results of the "PrincipalComponentsAnalysis" method applied to benchmark datasets including Fisher's Irises, MNIST and FashionMNIST:
- "PrincipalComponentsAnalysis" is equivalent to the "Linear" and "LatentSemanticAnalysis" methods when the data is standardized.
Examplesopen allclose all
Basic Examples (1)
Dataset Visualization (1)
Load the Fisher Iris dataset from ExampleData: