LocalOutlierFactor
statistics: LocalOutlierFactor
Local Outlier Factor model for anomaly detection.
A LocalOutlierFactor object stores a Local Outlier Factor (LOF)
model fitted to a set of observations, and detects anomalies among those or
new observations through the isanomaly method. Create a
LocalOutlierFactor object with lof or the class constructor.
The LOF of an observation compares its local density with the local density of its neighbors; a value near 1 indicates an inlier, whereas a value well above 1 indicates an outlier that lies in a sparser region than its neighbors.
See also: lof, LocalOutlierFactor.isanomaly
Source Code: LocalOutlierFactor
The LocalOutlierFactor class contains the following properties:
The LocalOutlierFactor class offers the following public methods:
LocalOutlierFactor: Mdl = LocalOutlierFactor (X)
LocalOutlierFactor: Mdl = LocalOutlierFactor (…, name, value)
Mdl = LocalOutlierFactor (X) fits a Local Outlier
Factor model to the N-by-P matrix X, whose rows are
observations and columns are variables, and returns a
LocalOutlierFactor object Mdl.
Mdl = LocalOutlierFactor (…, name, value)
takes the following Name-Value pairs.
| Name | Value |
|---|---|
'NumNeighbors' | the number of nearest neighbors, a
positive integer less than N. The default is
min (20, u - 1), where u is the number of unique
observations. |
'Distance' | the distance metric used to find neighbors,
one of the metrics accepted by pdist2 ('euclidean' by
default). |
'ContaminationFraction' | the assumed fraction of
anomalies in X, a scalar in [0, 1] (default 0). It sets
Mdl.ScoreThreshold to the 1 -
ContaminationFraction quantile of the anomaly scores of X;
when it is 0 the threshold is the maximum score and no training
observation is flagged. |
'Exponent' | the Minkowski distance exponent (default
2), used only with the 'minkowski' distance. |
'Cov' | the covariance matrix used only with the
'mahalanobis' distance. |
lof fits the same model and also returns the anomaly indicators
and the anomaly scores of the observations in X.
See also: lof, LocalOutlierFactor.isanomaly
LocalOutlierFactor: tf = isanomaly (Mdl, Xnew)
LocalOutlierFactor: [tf, scores] = isanomaly (Mdl, Xnew)
LocalOutlierFactor: […] = isanomaly (…, 'ScoreThreshold', t)