6.5. Unsupervised dimensionality reduction (original) (raw)

• User Guide
• 6. Dataset transformations
• 6.5. Unsupervised dimensionality reduction

If your number of features is high, it may be useful to reduce it with an unsupervised step prior to supervised steps. Many of theUnsupervised learning methods implement a transform method that can be used to reduce the dimensionality. Below we discuss two specific example of this pattern that are heavily used.

6.5.1. PCA: principal component analysis#

decomposition.PCA looks for a combination of features that capture well the variance of the original features. See Decomposing signals in components (matrix factorization problems).

Examples

• Faces recognition example using eigenfaces and SVMs

6.5.2. Random projections#

The module: random_projection provides several tools for data reduction by random projections. See the relevant section of the documentation: Random Projection.

Examples

• The Johnson-Lindenstrauss bound for embedding with random projections

6.5.3. Feature agglomeration#

cluster.FeatureAgglomeration appliesHierarchical clustering to group together features that behave similarly.

Examples

• Feature agglomeration vs. univariate selection
• Feature agglomeration