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.