| Interface | Description |
|---|---|
| Kernel |
It is a generic Kernel which can be setup by a measure with the data vector
|
| Class | Description |
|---|---|
| BrayCurtis |
BrayCurtis(X,Y) = 1-2*W/(sum(X)+sum(Y)), with W = sum_i[ min(x_i,y_i)]
|
| BrownCorrelation |
Jump up to: a b c Székely, Rizzo and Bakirov (2007)
Jump up to: a b c d Székely & Rizzo (2009)
http://en.wikipedia.org/wiki/Distance_correlation#Distance_correlation
The used strategy to handle missing value is to evaluate values with all available value (estimate means)
and try to infer the covariance
|
| Entropy |
Abstract class of entropy that need to use an estimator.
|
| Euclidean | |
| Hellinger | |
| HilbertSchmidt |
Compute the Hilbert-Schmidt independence criterion according to
the estimate in the paper "On Kernel Parameter Selection in Hilbert-Schmidt Independence Criterion" p.3
|
| JensenShannon | |
| Kendall |
Using Tau-b (adjustments for ties)
Knight's Algorithm
|
| KullbackLeibler |
Symmetric case ( [ KLD(P|Q) + KLD(Q|P) ] /2 )
|
| MutualInformation |
Mutual information that use the entropy formula:
I(X,Y) = H(X) - H(X|Y) = H(Y) - H(Y|X) = H(X) + H(Y) - H(X,Y)
|
| Pearson | |
| Spearman | |
| Steinhaus |
Steinhaus(X,Y) = 2*W/(sum(X)+sum(Y)), with W = sum_i[ min(x_i,y_i)]
|
| VarianceOfLogRatios |
Variance of log ratios scaled to [0;1]: 1-exp(-sqrt(D(x,y))) according to Aitchison
where D(X,Y) is the Variance of log ratios.
|