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Local Outlier Factor

Local Outlier Factor (LOF) measures the local deviation of density of an unknown sample with respect to its k nearest neighbors from the training set. As such, LOF only considers the neighborhood of an unknown sample which enables it to detect anomalies within individual clusters of data.

Interfaces: Estimator, Learner, Scoring, Persistable

Data Type Compatibility: Depends on distance kernel

Parameters

#	Name	Default	Type	Description
1	k	20	int	The k nearest neighbors that form a local region.
2	contamination	null	float	The proportion of outliers that are assumed to be present in the training set.
3	tree	KDTree	Spatial	The spatial tree used to run nearest neighbor searches.

Example

use Rubix\ML\AnomalyDetectors\LocalOutlierFactor;
use Rubix\ML\Graph\Trees\BallTree;
use Rubix\ML\Kernels\Distance\Euclidean;

$estimator = new LocalOutlierFactor(20, 0.1, new BallTree(30, new Euclidean));

Additional Methods

Return the base spatial tree instance:

public tree() : Spatial

References

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1. M. M. Breunig et al. (2000). LOF: Identifying Density-Based Local Outliers. ↩