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Gaussian Random Projector#

Random Projection is a dimensionality reduction technique based on the Johnson-Lindenstrauss lemma. It uses random matrices to project feature vectors onto a target number of dimensions. The Gaussian Random Projector utilizes a random matrix sampled from a smooth Gaussian distribution which projects samples onto a spherically random hyperplane through the origin.

Interfaces: Transformer, Stateful

Data Type Compatibility: Continuous only

Parameters#

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

Example#

use Rubix\ML\Transformers\GaussianRandomProjector;

$transformer = new GaussianRandomProjector(100);

Additional Methods#

Estimate the minimum dimensionality needed to satisfy a max distortion constraint with n samples using the Johnson-Lindenstrauss lemma:

public static minDimensions(int $n, float $maxDistortion = 0.5) : int
use Rubix\ML\Transformers\GaussianRandomProjector;

$dimensions = GaussianRandomProjector::minDimensions(5000, 0.2);