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Principal Component Analysis#

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

Interfaces: Transformer, Stateful

Data Type Compatibility: Continuous only

Parameters#

# Param Default Type Description
1 dimensions None int The target number of dimensions to project onto.

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

Example#

use Rubix\ML\Transformers\PrincipalComponentAnalysis;

$transformer = new PrincipalComponentAnalysis(15);

References#

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