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.
Data Type Compatibility: Continuous only
|1||dimensions||int||The target number of dimensions to project onto.|
use Rubix\ML\Transformers\PrincipalComponentAnalysis; $transformer = new PrincipalComponentAnalysis(15);
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
- H. Abdi et al. (2010). Principal Component Analysis.