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Principal Component Analysis#

Principal Component Analysis (PCA) is a dimensionality reduction technique that aims to transform the feature space by the k principal components that explain the most variance. PCA is used to compress high-dimensional samples down to lower dimensions such that they would retain as much information as possible.

Interfaces: Transformer, Stateful

Data Type Compatibility: Continuous only

Parameters#

# Param Default Type Description
1 dimensions int The target number of dimensions to project onto.

Example#

use Rubix\ML\Transformers\PrincipalComponentAnalysis;

$transformer = new PrincipalComponentAnalysis(15);

Additional Methods#

Return the amount of variance that has been preserved by the transformation:

public explainedVar() : ?float

Return the amount of variance lost by discarding the noise components:

public noiseVar() : ?float

Return the percentage of information lost due to the transformation:

public lossiness() : ?float

References#

  • H. Abdi et al. (2010). Principal Component Analysis.