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Sparse Random Projector

A database-friendly random projector that samples its random projection matrix from a sparse probabilistic approximation of the Gaussian distribution. The term database-friendly refers to the fact that the number of non-zero operations required to transform the input matrix is reduced by the sparsity factor.

Interfaces: Transformer, Stateful, Persistable

Data Type Compatibility: Continuous only

Parameters

#	Name	Default	Type	Description
1	dimensions		int	The number of target dimensions to project onto.
2	sparsity	0.66	float	The proportion of zero to non-zero elements in the random projection matrix. If null, sparsity factor will be chosen automatically.

Example

use Rubix\ML\Transformers\SparseRandomProjector;

$transformer = new SparseRandomProjector(30, null);

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\SparseRandomProjector;

$dimensions = SparseRandomProjector::minDimensions(10000, 0.5);

References

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1. D. Achlioptas. (2003). Database-friendly random projections: Johnson-Lindenstrauss with binary coins. ↩

2. P. Li at al. (2006). Very Sparse Random Projections. ↩

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Last update: 2021-03-03