| Package | Description |
|---|---|
| de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic |
Distance from probability theory, mostly divergences such as K-L-divergence,
J-divergence, F-divergence, χ²-divergence, etc.
|
| Class and Description |
|---|
| ChiDistanceFunction
χ distance function, symmetric version.
|
| ChiSquaredDistanceFunction
χ² distance function, symmetric version.
|
| FisherRaoDistanceFunction
Fisher-Rao riemannian metric for (discrete) probability distributions.
|
| HellingerDistanceFunction
Hellinger metric / affinity / kernel, Bhattacharyya coefficient, fidelity
similarity, Matusita distance, Hellinger-Kakutani metric on a probability
distribution.
|
| JeffreyDivergenceDistanceFunction
Jeffrey Divergence for
NumberVectors is a symmetric, smoothened
version of the KullbackLeiblerDivergenceAsymmetricDistanceFunction. |
| JensenShannonDivergenceDistanceFunction
Jensen-Shannon Divergence for
NumberVectors is a symmetric,
smoothened version of the
KullbackLeiblerDivergenceAsymmetricDistanceFunction. |
| KullbackLeiblerDivergenceAsymmetricDistanceFunction
Kullback-Leibler divergence, also known as relative entropy,
information deviation, or just KL-distance (albeit asymmetric).
|
| KullbackLeiblerDivergenceReverseAsymmetricDistanceFunction
Kullback-Leibler divergence, also known as relative entropy, information
deviation or just KL-distance (albeit asymmetric).
|
| SqrtJensenShannonDivergenceDistanceFunction
The square root of Jensen-Shannon divergence is a metric.
|
| TriangularDiscriminationDistanceFunction
Triangular Discrimination has relatively tight upper and lower bounds to the
Jensen-Shannon divergence, but is much less expensive.
|
| TriangularDistanceFunction
Triangular Distance has relatively tight upper and lower bounds to the
(square root of the) Jensen-Shannon divergence, but is much less expensive.
|
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